AI in Marketing & Sales - Avausinfo.avaus.fi/rs/878-EHZ-827/images/Avaus_Cookbook_oct_2019.pdf ·...

AI inMarketing

& SalesYour pragmatic guide to using customer data and algorithms to get more results out of your current

marketing and sales activities.

The Avaus Cookbook

Table of Contents

Foreword……………….…………..1

Equipping your kitchen………......2

Recipes………………….………………...3

Templates…….……….…………...…...4

Join the #algoleap..………….………..5

Foreword

A bowl of data, your customer interaction channels and a pinch of AI...

As leaders in marketing, sales and customer experience, we all are under pressure to create more results with the same - or even fewer resources. Being data-driven promises the ability to use your organisation’s data and improve outcomes with the help of digitalised tools and methods.

The challenge is - it requires that you know how to apply the data to create those results. And once you know - you have to overcome the hurdles of siloed organisations, low data literacy, change resistance and the like. Thus - we might have all the ingredients in place, but still not manage to create an extraordinary meal! Unfortunately, the world around us is also changing - to the more complex. The data we can use in our data-driven initiatives takes on a more complex form: the channels we can use for activating data are growing in number and complexity (hello voice assistants!).

To help out fellow practitioners and business decision-makers alike, we at Avaus have collected a set of 50 recipes to help you take the first steps to becoming increasingly data-driven - in any and all aspects of what you do.

Many of the recipes are very operative in nature - but that is where you take the first steps on your bigger “algo leap” as we like to call it. We have learned first hand that the first thing that needs to happen is for all of your team members to start thinking with an “algorithmic” mindset on the business problems they are addressing every day.

The recipes showcase the three ingredients to successful data-driven operations that we at Avaus have coined: “Data, Algo, and Action”.

Using the recipes should be seen as a first step on the journey to become “AI first” - once you have some exciting results and a team that knows how to make it happen - it is time to think about how you can scale up the capability and make it a core operating principle.

At Avaus, we strongly believe that in the future, every successful company will be powered by algorithms. So you better start now.

Best of luck and happy cooking!

Emma Storbacka Group CEO

ForewordWhere in your business will a 10X improvement matter most?

Transitioning from rules-based decision-making to algorithmic decisioning at scale will enable you to improve your business results by up to 10X. The question is - where can you drive the most value for your current business objectives?

Focus where it matters mostTaking the initial steps from ad-hoc to automated actions, and the next step of going from rule-based to algorithmic decisioning will have not only an incremental impact on business results but is likely to create a step-change in results and efficiency. Forerunners who go from rules-based to algorithmic decisioning report e.g. conversion improvements of up to 10x. The first step for any business leader is to address the current business priorities and find the levers where the biggest impact can be made by starting to experiment with data and AI.

Start small, then scaleAlthough many of the recipes in this cookbook are very operative, when applied where it matters most will produce significant results in top or bottom-line performance. Once the approach has been proven to generate results, the key question will be how to support scaling up your efforts.

You are never doneAs a business practitioner, it is important to realise that putting an algorithm in place to improve business results is not a “checkbox” type of thing - once done you can move on to the next development project. In the future, all successful companies will rely heavily on algorithms - and continuous refinement and improvement will be core to how you run your business. Large enterprises should learn from the way that YouTube, Facebook, Spotify and Netflix continuously roll out new algorithms and services based on your data, but also continuously improve, tweak and test the algorithms that are fundamental to their business metrics.

1. GROW THE TOP LINE

Growcustomer

base

Increase spend per customer

Improve customer

experience

Decrease cost of sales

Decrease cost to serve

Improve productivity

2. IMPROVE EFFICIENCY

© Avaus

5xConversion rate

improvement with algorithmically chosen

audiences

Foreword

Going from rules-based to algorithmic

2.7% Hit rate

improvement for outbound calls

+5% Increase in weekly

grocery spend due to personalised offers

+300%RoAS improvement

(Return on Advertising Spend)

+100% Conversion

improvement with algorithmic

personalisation(Email CTO / CTR)

+800% Increase in coupon

redemption with personalisation

Sources:Avaus project outcomes, Analytical PoCs and industry benchmarks © Avaus

Data: 40%Data preparation

ForewordMore than 50% of analytics initiatives fail to deliver value - because of limited focus on operationalisation

Algo: 20%Model development

Action: 40%Model

operationalisation

● Data validation● Data collection● Data transformation● Configuration

environment

● Feature Engineering● Parameter tweaking● Target label testing● Model verification

● Model deployment● Model maintenance

(update and upgrade)● Business actions

automation● Change management● Process tweaks

Multiple industry analysts confirm that between 50-80% of analytics initiatives fail to deliver the business value planned. One of the main reason for this is the inability to successfully operationalise the algorithms. In other words - going from Data to Algo is easy, going from Algo to Action is hard.

End-to-end PoCs are a mustThe 'Action' piece of your end to end process is what will deliver business results, or fail to do so if not planned for. A typical first step once data has been gathered and made available for analytical modelling is to launch a disparate set of PoCs - all with an algo-only focus. While this is a great start - it will not create long-term success. To ensure success in the long run, it should be agreed early on as to what a successful PoC should look like, and what taking it to the Pilot stage and then production should entail - from an end to end perspective. A successful analytical PoC should be seen as a multi-phased approach.

This ensures that not only data and algorithms are able to deliver value as planned, but also that the organisation is ready to operationalise the model - both technically and also from a process and change perspective.

Focus on operationalisationThe analytical data science work typically gets most of the attention, but where the business impact happens is in the operationalisation of the models. This includes both technical deployment and maintenance of the models, and also integrations to business processes. In the case of Marketing and Sales - typically model outputs need to be integrated with the solutions in your martech stack and customer processes running across multiple customer interfacing platforms.

A good rule of thumb, resource and effort-wise, is that Data and Action (Operationalisation) take up approximately 80% of time spent, and algorithm modelling only around 10-20%. To increase efficiency in both “Data” and “Action” steps, a Machine Learning framework and standardisation of the operationalisation processes will help.

Effort required per phase:

© Avaus

Table of Contents

Foreword……………...……………..…..1

Equipping your kitchen……..2

Recipes……………………………….…...3

Templates…….…………………....…...4

Join the #algoleap..………….………..5

The ingredients for any data-driven recipe: Data, Algo and Actions

The world of data-driven activities is complex, and often we get stuck with decision fatigue as we hear words like Artificial Intelligence, Machine Learning, Programmatic, Automated, Data-driven and Intelligence. At Avaus, we suggest a simplified framework:

[ Data x Algo x Action ]

In essence, it is nothing new. What makes a real difference is adding more intelligence and automation to the “Algo” part and take the leap from rules-based to algorithmic decisioning. In this section, we have provided a brief overview of what Data, Algo and Actions are and can be - and they are also the main ingredients of the recipes that follow.

Only by combining the three ingredients will you be able to create results out of your data - and that is also where the complexity resides - due to both organisational as well as competence related restrictions.

[Data] is the fuel of your data-driven activities. All companies have more data available for creating data-driven actions than they think. On the other hand, all companies need to collect more data in order to be competitive in the future. With non-tabular data such as images, voice, video becoming easier to process with the help of Artificial Intelligence platforms (such as e.g. Peltarion) - most companies need a data strategy that also takes unstructured data into account.

[Algo], or algorithms, are the rules that define what we want the data to do. Depending on sophistication they can be simple rules, such as “If this then that”, or they can be more complex, such as a recommendation engine. Going from rules-based to algorithmic decisioning is what will add the greatest efficiency gain, although simple rules-based automation of processes might already be enough to bring significant business improvements. Adding self-learning elements to your model will enable it to become better over time as it is live and producing decisions.

[Actions], are the outputs, processes and activities you want to automate with your data. The low hanging fruits typically reside within your marketing and sales domain, but can extend to all aspects of your business, such as pricing, production and inventory.

© Avaus

Data IngredientsWhat data do you have and is it available for driving automated actions?

Customer frontline data❏ Customer service and sales

rep activities❏ Customer service and sales

rep insights❏ Leads and opportunities

Location data❏ Device location data❏ In-store location tracking

Unstructured data❏ Voice❏ Image❏ Text❏ Video

Operational data❏ Capacity❏ Workforce information❏ Competitor data

2nd party dataAttitude or descriptive surveyed data ❏ Attitudes & interests❏ Market segments❏ Price sensitivity

Social data❏ Facebook / Instagram

activity❏ Linkedin activity❏ Snap activity❏ Pinterest activity❏ Social listening

Partnership data❏ Transactional / behavioural❏ Descriptive / attitude

3rd party dataAggregator data❏ Unidentifiable web data

(Google analytics, Adobe analytics etc)

❏ Marketplace data❏ Firmographic data❏ Mosaic / Geodemographics

...and the list goes on!

Start with what you haveIn order to get started, most enterprises already have the data needed - although it might be scattered or incomplete. Fill out the list on the right - what have you already collected? What might you need in the near future?

Create a data strategyTo collect data takes time, and only when you have enough of it will you be able to create the algorithms and actions that you might need in the future - so don’t put off gathering today what you might need in the future!

Collect, unify and structureData will come from a variety of sources and to act on it needs the unification of customer profiles across channels and touchpoints. This can require both rules-based and probabilistic methods and requires strict MDM and data governance to maintain quality.

What is your current data availability?✓ Available today✓ Collected but not availableX Not collected

1st party dataTransactional data ❏ Offline – In store❏ Online – In store

Behavioural data❏ Web❏ Social❏ Service usage❏ Offline – In store❏ Online – In store❏ NPS / C-SAT scores

Demographic data❏ Age, gender, address etc❏ Demographic segments❏ Propensity segments❏ Observational data

Campaign data❏ Email metrics❏ Digital advertising metrics❏ Social advertising metrics❏ SEM metrics❏ Image content❏ Video content❏ Text content

Data availability - The basics

© Avaus

Algo IngredientsWhat algorithms are you using to develop your business?

Algorithm Library - The basics

Business strategies (to optimise against)❏ Customer segmentation❏ Customer lifetime value❏ Customer profitability❏ Customer uplift model❏ Operating efficiency

Experience & Journeys❏ Customer life-cycle model❏ Purchase prediction❏ Churn prediction❏ Churn reason ❏ Winback propensity❏ Engagement & loyalty

scoring

Customer Service❏ Customer service AI❏ Customer feedback &

sentiment analysis❏ Next best action❏ C-SAT likelihood❏ Issue classification❏ Issue routing❏ Speech-to-text

Media & advertising❏ Social algorithms❏ Programmatic algorithms❏ Audience algorithms❏ Channel selection

automation❏ Campaign prediction

algorithms❏ Optimisation algorithms

Offers❏ Next best action (serve, sell,

retain)❏ Recommendation engine❏ Personalised offers❏ Competitor pricing/scraping

algorithms❏ Supplier negotiation

automation❏ Offer planning and/or

logistics

Assortment & Pricing❏ Assortment rationalisation

algorithms❏ Category prediction

algorithms❏ Spacing and store planning

algorithms❏ Cannibalisation algorithms❏ Dynamic pricing

Creativity & Content❏ Copy optimisation

algorithms❏ Image optimisation

algorithms❏ Cognitive analysis /

Personality matching❏ Video creation algorithms❏ Voice algorithms❏ Asset automation❏ Localisation automation❏ Dynamic content

optimisation algorithms

What does your current algo library look like?✓ Core algo, optimised continuously✓ RudimentaryX Missing- Not relevant

From PoCs to Pilots, and Pilots to ScaleDesigning end-to-end PoCs is crucial (i.e. incorporating also the “Action” element). The first roll-outs of a new algorithmically driven process should be piloted in a contained environment, to learn and understand what needs to be taken into account when scaling up. Keep in mind from the start the end-state of scale when designing the PoC and Pilot. This avoids reaching a roadblock when finally being ready to create business value.

ML Framework: Automate what you can, standardise what you can’tAs most companies will need tens or hundreds of algorithms and connections, efficiency in model production and deployment is needed. A Machine Learning Framework is usually needed, which determines both technical development standards as well as e.g. naming conventions and documentation processes. (Read more on avaus.com)

Buy or build?Many single-purpose algorithms are built-in to modern SaaS-solutions and should be used where possible. Most companies over time have a set of strategic algorithms that are core to business success - and therefore should not be bought as black-box solutions. (Read more in this blog on avaus.com) © Avaus

http://www.avaus.com/blog/machine-learning-framework-for-marketing

http://www.avaus.com/blog/buy-or-build-algorithms

Getting the Martech Ferrari out of the garageSince 2015, marketing and sales technologies have become household items in every marketing team - but a shocking 50% of them report they don’t see the business results commensurate with the Martech investments. One identified reason for this is remaining within the “Actions” domain and not connecting more data and decisioning algorithms to drive the digital customer experiences.

Integrating your stack - goodbye channel silos!Where many data-driven initiatives can be started within a single channel - and that typically is the first step - marketing executives should have a clear strategy for reaching a level of omnichannel personalisation and data-drivenness over time. Customers expect a seamless experience - and the data and algorithms following the customers over channels are where customer experience expectations either are met - or not.

Channel activation

Omnichannel IngredientsIn which channels are you activating your data and algorithms?

In which channels are you able to activate model outputs today?✓ Real-time✓ Batch loadsX Missing- Not relevant

WebsiteImprove conversion by personalising contents, offers and service experience.

AppCross/up-sell using in the moment communication and personalisation.

EmailReduce spam & improve efficiency by sending the right emails, to the right people, at the right time.

Call centerCost-reduction by automating service functions. Use data as a catalyst for marketing and sales.

SocialIncrease ROAS by using marginal revenue models & automated audience segmentation (& spend).

ProgrammaticSame as social, but consider increasing the extent of automated content to improve efficiency.

DOOHUse algorithmic models for improved timing of out-of-home messaging.

TVIncrease ROAS by algorithmic timing, interaction and media allocation models.

POSRecommendations and automated tactics to increase sales in all point of sales.

To what extent is the channel part of a connected omnichannel experience?✓ Fully✓ Partly / some aspectsX Missing- Not relevant

© Avaus

Kitchen layoutDo you have the capabilities to scale your data-algo-action initiatives?

1.Compliance & ConsentHow much of your data do you have consent to utilise for developing algorithms? How are you collecting and maintaining consent across channels?

2.Data Collection StrategyMost companies today do not yet have a data strategy for customer data. As training data will be the scarcest resource in any AI initiative - collecting data today will ensure success in the future.

3.Solutions Architecture built for scaleIn order for your data to drive actions seamlessly, your entire data-driven architecture needs to be designed for it. Real or near-real time data access for models to produce decisions in milliseconds might be the need for some - unifying customer data across sources is vital for most.

4. Analytical environmentHaving the right analytical environment will play a big part in what is possible to execute - and how fast. Ability to leverage pre-built models and frameworks is key - democratisation of data science will become the future way for most organisations.

5. Machine Learning FrameworkIn order to get scalability into analytical development, a consistent framework for both technical development as well as processes and ways of working needs to be defined.

6. Team and skillsTalent is scarce and short-lived in many of the bottleneck areas of data-driven initiatives. To be able to scale you need a plan in place for long term team and skills development, as well as a change management efforts to get everyone to take the “Algo Leap” into the age of AI.

7. Data GovernanceData governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. For marketing and sales organisations, this topic might be new, and needs to have a business owner that drives development in this area forward as a business-critical process.

8.Scalable Content Production Personalisation is one of the key data-driven strategies many marketers start out with. In order to create the needed quantities of content, more of the content creation needs to be automated.

© Avaus

Kitchen staffDo you have the right ways of working to turn your data into business results?

1. Focus on business resultsEstablish a clear target for the team with a North star KPI which guides every decision. Ensure tactical results are always translated to pique the interest of a CFO.

2. Full-stack, cross competence teamEnsuring that the team has the needed core competencies, so that it can autonomously perform 80% of the tasks needed. Expertise should cover the entire data-algo-action process.

3. Aiming for speed over perfectionThe team leader needs to remove blockers and obstacles from the team to ensure speed. Speed is important since speed accelerates learning and provides faster time to value.

Focused on solving as many problems as

possible

Basing action on pre-defined plan

I-shaped profiles working alone or with

same competence

Low level of testing, building to 80% ready

before go-live

Ambition in the process rather than the results

Focused on solving problems that impact a business KPI/target

Basing actions on data and results

T-shaped profiles working as a full-stack team

Fast pace experimentation, building 20% at a time to test

Ambition in showing “real” business results

How would you rate your way of working?

If your answers are leaning more to the left, you might need to review your way-of-working to ensure that your teams do not get stuck

© Avaus

Kitchen safetyAre your GDPR and Data processes secure?

General GDPR & Data Processing Checklist Make a new best friend❏ Identify and connect early on with your GDPR or DPO representative❏ Get a clear understanding of what the DPO needs in terms of

documentation and follow-up❏ Make sure you have DPAs in place for processors and potential

subcontractors

Data Awareness ❏ Be aware of what categories of Personal Data are processed ❏ Be aware of what categories of Data Subjects are undergoing

processing❏ Be aware for what purposes you can process Personal Data

Data processing & consent❏ Ensure you have consent to collect the data you aim to use or collect❏ Ensure that you have consent to process the data for the specific use

case (even though you have consent to collect, it does not mean that you have consent to process for any purpose)

❏ Clarify how you are allowed to move, engineer and expose data (internally and externally)

Personalisation should be at the heart of your organisation in order to provide superior services to your customers. This means that you will need to process personal and sometimes sensitive data.

Not only a compliance issue - trust is currencyAs data becomes the most important factor for enterprises looking to future-proof their businesses, consumer consent is crucial. If consumers don’t trust that you are using their data in their best interest, you might have a hard time collecting consent. Thus, “giving data back” and having a clear answer to how you are responsibly using the end-customers’ data is key.

Complying with both GDPR, as well as your ethical standardsCustomer data is not only a tool for improved business results, but can also be a tool for delivering on your sustainability agenda - create a plan for how to leverage your data for driving sustainability impact from every interaction with your clients

© Avaus

Table of Contents

Foreword……………...………..………..1

Equipping your kitchen...............2

Recipes……………………...….....3 Templates…….…………….……...…...4

Join the #algoleap..……….…………..5

Table of Content

50 data-algo-action recipes to turn your data into results

1. Website & eCommerce01 Automate personalisation of web content02 Display dynamic prices to maximise profit03 Present relevant product reviews per visitor04 Algorithmic image classification for product feed05 Personalise search results in ecommerce

2. Email & SMS06 Segment email subscribers based on engagement07 Automate product recommendations08 Optimise open rate with subject line response09 Predict optimal message send out frequency10 Individual send times for improved response11 Predict and treat unsubscribers differently

3. Chat & Mobile App12 Predict likelihood to download App13 Optimise push notifications for higher CTR14 Optimise in-app actions based on predicted CLV15 Predict churn risk and automate app retention16 Personalised upsell in chat (live or a chatbot)17 Predict NPS based on service interactions18 Image recognition in a chatbot for tailored services19 Personalise chatbot interaction 20 Implement a smarter chatbot to reduce costs

4. Biddable media21 Use knowledge about existing customers to find new22 Target based on prospect potential to convert23 Optimise spend of bought media based on uplift24 Next best offers in bought media25 Precision retargeting based on purchase propensity26 Prevent churn with a targeted surprise action27 Create automated content marketing targeting28 Personalise based on psychographic segment 29 Identify potential influencers 30 Base your keyword selection on expected CLV

5. Call Center / Customer service31 Next best offer (NBO) for customer service32 Algorithmic quality assessment of service33 Tailor sales calls based on recipient personality34 Personalised coach for call centre agents35 Reduce average handling time

6. B2B Sales & Marketing 36 Predict need for maintenance and automate sales37 Predictive lead/prospect scoring38 Analytical scoring of deal quality39 Accelerate pipeline velocity / win-rate40 Dynamic pricing for maximised profit41 Predict buying window and product purchase42 Capacity and sales optimisation

7. Omnichannel & Strategy43 Predict major life events44 Create better audiences with response modeling45 Use budget better with marketing mix modelling46 Multi-touch attribution model for omnichannel47 Next Best Action for omnichannel orchestration48 Content sequencing for optimal customer journey49 Churn risk classification and “vaccine” actions50 Product library categorisation

© Avaus

Website & eCommerce

01 Automatic personalisation of content on web02 Display dynamic prices per visitor to maximise CLV

03 Presenting the most relevant product reviews04 An algorithm to solve your product feed issues

05 Personalising the search results of your ecommerce

Recipes

Recipe no: 01

Automate personalisation of web content !

ComplexityData ***** Algo ***** Action *****

Data1. Content for testing (e.g. images or text)2. Content semantics (elements / style etc)3. Click-stream (behaviour on web)4. Conversion point (e.g. sales)

Algo1. Predicting most relevant content to drive a

receiver towards the ultimate conversion point

Action1. Present each customer with the most relevant

content2. Set up test and control to feed back results and

continuously improve model

〰〰〰〰

Is your website personalised automatically, and at scale?Manual A-B testing of content is very time consuming and slow. In addition it’s not very data-driven as the variables are typically selected by a human and targeted to a receiver based on pre-set rules and assumptions. Instead, this recipe enables you to use machine learning to drive the most uplift in revenue for each segment, typically enabling you to drive 20-60% higher conversion on your website.

© Avaus

Recipe no: 02

Display dynamic prices to maximise profit !


Data1. Historic conversions and price points2. Context of communication/conversion3. Customer information4. In-house capabilities and restrictions

Algo1. Self-learning algorithm such as reinforcement

learning based on historical data

Action1. Dynamic display of price on website, banners,

or eCommerce2. Minimise discounts required to drive

conversions3. Compare results between the control group

and the audience which has seen the dynamic prices

〰〰〰〰

Are you maximising your pricing for higher margins?Being able to have flexible prices and base them on demand, in-house capabilities, restrictions, ability to deliver as well as the customers’ willingness to pay will allow you to tap into revenues hidden by fixed or rules-based pricing. Revenue can be increased both by being able to charge a higher price when possible and also to sell more when the willingness to pay is below the static price.

revenue

DEMAND

p1

revenue

p1

p2

p3

DEMAND

extra revenue

STATIC PRICING DYNAMIC PRICING

© Avaus

Recipe no: 03

Present relevant product reviews per visitor !


〰〰〰〰

Are you giving relevant arguments about your products & services?Products can have a lot of reviews, but not all of the reviews and comments are relevant to every customer. The most relevant reviews per visitor should be presented first based on the customers profile (ex. environmental interest, price sensitivity or technical interest) to increase sales conversion rate by 5-30%.

Data1. Customer data (product and purchase history,

other customer attributes of interest such as city, payment method, style, age)

2. Product data and product categories3. Web/App behaviour data (clickstream)4. Product reviews feed (text / review numbers)5. Feedback data on reviews (was this review

helpful?)

Algo1. Predict the most relevant product review for

each web visitor

Action1. Create a dynamic product review feed on the

website CMS which uses the algorithm to order the reviews based on the visitor (most relevant review on top)

Match / Relevancy:

High

Low

© Avaus

Recipe no: 04

Algorithmic imageclassification for product feed !


〰〰〰〰

Are you struggling with an unlimited product feed?It is not uncommon for companies to have a very low data quality in their product feeds. Over time, the requirements have constantly changed for how to classify products into categories and other tags. If this problem is not something that is easily fixed, e.g. because of the sheer amount of products available, then this algorithm will help you to classify products by image instead.

Data1. All product images2. Web engagement data (clicks, views, etc)3. Transactional data (revenue, conversions)

Algo1. Deep-learning algorithm for image

recognition. Based on product images, will find products similar to customer preference based on:

a. Added to cart, viewed, purchased, etc.

Action1. Use the algorithm output to target customer

with relevant products for example starting with simple upsell recommendations on the web front page based on the products they previously viewed

© Avaus

Recipe no: 05

Personalise search results in ecommerce !


Data1. Online behaviour2. Search history and click through data3. Product hierarchies4. Purchase history5. Customer preferences & demographics

Algo1. Collaborative filtering2. Propensity models for likelihood to buy

Action1. Display search results in terms of customer

relevancy (most relevant products first e.g. based on style or preference)

2. Use search results for customer development objectives such as up- and cross-sell.

〰〰〰〰

Are you making it easy for a customer to find products?With one of the main attractions of e-commerce being variety through a vast assortment, enabling customers to navigate through options is key. With a predictive and intuitive algorithm for searching products based on the purchase history, individual behaviour and the customer strategy, the shopping experience can be made more convenient whilst at the same time search can be used for targeting and upselling.

Results vary based on whois searching and when

?

?

?

Search results

© Avaus

Email & SMS06 Segment email subscribers based on engagement

07 Automate product recommendations to increase conversion08 Optimise open rate with subject line response prediction

09 Predict optimal email / push send out frequency10 Individual send times for improved response

11 Predict and treat unsubscribers differently

Recipes

Recipe no: 06

Segment email subscribers based on engagement !〰〰〰〰

Do you know when is the best time to engage?Increase your knowledge about your subscribers by grouping them in targetable segments by classifying them by their historical engagement. This allows marketers to monitor the health of their subscriber base & take more targeted action.

Likelihood to open

Like

lihoo

d to

clic

k Least likely to openLeast likely to click

More likely to openLeast likely to click

Least likely to openMost likely to click

Most likely to openMost likely to click

Winback/Dormant Window shoppers

Selective subscribers Loyalists


Data1. Individual email engagement data, clicks and

opens.

Algo1. Predicted likelihood to engage (open / click)2. Classification into segments

Action1. Monitor health of customer base2. Evaluate if dormant customers are likely to

come back3. Follow-up with targeted actions when window

shoppers & selective subscribers clicks or opens

4. Keep loyalists happy

© Avaus

Recipe no: 07

Automate product recommendations to increase conversion !〰〰〰〰

Are you mass marketing your products?Crafting an email is still a manual struggle for many companies. Products selected for emails are usually chosen randomly and often have little to do with the receiver. Product recommendations allow us to flip the perspective and present relevant products under changing circumstances. This creates both better customer experience as well as higher conversion rate (up to +70%).


Data1. Product feed: product, characteristics about

product2. Web & Email engagement data3. Purchase history

Algo1. Best product to recommend under different

circumstances, can be based on product, customer or trending

2. Response model: subject to response likelihood

Action1. Create a dynamic content module(s) in your

emails to automatically generate the relevant product recommendations from your product feed ex. in the after purchase email

© Avaus

Recipe no: 08

Optimise open rate with subject line response prediction !


Data1. All sent emails and engagement per recipient2. Subject lines used3. Send timing of campaigns4. Open timing of recipients

Algo1. Likelihood to open2. Subject line response likelihood

Action1. Create segments based on the response

propensities to identify relevant subject line / content

2. In first phase, start by changing the order of the content on your emails and using different subject lines based on the order of the content

3. Exclude recipients with low expected response rate

〰〰〰〰

Do you know if a customer will open your email?Typically open rates for emails are between 5 and 20% - which means that between 95-80% of the emails you are sending are unnecessary - i.e. waste. This prohibits you from sending other, more relevant messages to these respondents (you don’t want to spam them, right?), and also might cost you money.

© Avaus

Data1. All sent emails and engagement per recipient2. Unsubscribe data

Algo1. Predictive model for open rate2. Predictive model for unsubscription rate

Action1. Automatically modify email frequency

according to each user’s preference for higher engagement metrics

a. Test how to increase frequency through optimizing and personalizing subject lines

b. Identify passive customers and try other channels to increase overall engagement

Recipe no: 09

Predict optimal email /push send out frequencyAre you sure that you are sending the right number of emails?Send too few emails and you could be missing sales opportunities or risk not being top-of-mind. Send too many emails and risk oversaturating your customers, possibly annoying them to the point of unsubscribing or churning.


〰〰〰〰

© Avaus

Recipe no: 10

Individual send times for improved response !〰〰〰〰

Do you know the best time for each recipient?Usually a marketing department selects at what day & time a newsletter or an email campaign goes out, based on what has worked best - on average. With this approach, emails should go out at the optimal time for each recipient, i.e. when they’re most likely to open. This enables you to get up to +20% higher ROI with the effort your already are investing.


Data1. Historical data of email send outs per

customer2. Historical data of email open time stamps per

recipient

Algo1. Predictive model for when a customer is most

likely to open

Action1. Select an interval for when the emails all need

to be sent, usually 24 hours2. Allow the algo to decide when to send emails

or notifications based on each contact

© Avaus

Recipe no: 11

Predict and treat unsubscribers differently !〰〰〰〰

Struggling with high unsubscription rates?The average email list decays by 25-30% each year, either through unsubscribes or non-engaged users. One part of combating this is to be able to predict the churning part of your subscribers. These insights are valuable in order for marketeers to treat potential un-subscribers differently, to reduce the risk of losing them entirely, as marketing consent can easily be lost in the same process.


Data1. List membership2. Email activity with timestamps3. Segment membership (Eg. VIP member)

Algo1. Predictive model for the degree of certainty

that a specific email subscriber will unsubscribe

Action1. Remove high-risk recipients from

non-operational email programs2. Experiment with multiple treatment

approaches for high-risk unsubscribers to define what (if any) changes should be made to content

3. Launch other non-email marketing programs targeting potential unsubscribers, that over time may reduce risk of unsubscription

No thx! Don’t think so!

I am done!Stop!

Nop!

© Avaus

Chat & Mobile App12 Predict likelihood to download App

13 Optimise push notifications for higher CTR14 Optimise in-app actions based on predicted CLV

15 Predict churn risk and automate app retention16 Personalised upsell in chat (live or a chatbot)

17 Predict NPS based on service interactions18 Image recognition in a chatbot to create tailored services

19 Personalise chatbot interaction with sentiment analysis20 Implement a smarter chatbot to reduce cost of service

Recipes

Recipe no: 12

Predict likelihood to download App

!


〰〰〰〰

Do you know which of your current customers are most likely to download your app?Predicting exactly which of your existing customers are the most likely to download your app within the next 15-30 days makes your efforts more efficient. Marketing of app using a propensity model of new App users typically increases conversion rate from marketing by 20-60% and reduces wasted interaction.

Data1. Customer past interactions2. Customer demographic data3. Customer engagement data (e.g. opens, clicks

in email channel)

Algo1. Propensity to download the App in next 30

days2. Additional optimisation: Propensity to

download if marketed to in each channel

Action1. Use email and SMS channel to target most

potential customers with an attractive offer to download the app. Tip: Give an exclusive offer or benefit which is only available in App.

2. Use target group and extend audience using lookalike audiences with 3rd party data in bought channels

Who is most likely to

convert?

© Avaus

Are your push notifications tailored or same-same?Push notifications used as a mass-marketing channel have a typical response rate of 10-20%. With an optimised target group and tailored messaging, marketers can reach up to 60% higher CTR, and customers benefit from a less intrusive and more relevant customer experience (and keep notifications turned on!).

Recipe no: 13

Optimise push notifications for higher CTR !


Data1. All sent push and engagement per recipient2. Subject lines used3. Text or Images used

Algo1. CTR prediction2. Sentiment and tone analysis3. Subject line response likelihood

Action1. Identifying the kind of communication your

recipients find interesting, and focus on that.2. Tailor communication: Define segments on

type of message and communicate based on previous responses.

3. Exclude recipients with lowest likely response rates

〰〰〰〰

AY

B

A

BY

N

N

© Avaus

Recipe no: 14

Optimise in-app actions based on predicted CLV !


〰〰〰〰

Do you know which of your potential & newly acquired customers will be most valuable in the future?In app performance marketing everything happens fast. Channel attribution data tells a lot about user intention and motivation. Users converted from aggressive marketing campaigns are likely to churn. By combining attribution data with app user data we can predict CLV during early app usage. Thus marketers can start optimising media budgets more wisely and focus tailored user experiences and rewards to key users in the early state.

A2 000

B4 000

D25 000

C8 000

Users are likely to convert

Users will possibly convert

Users have a low margin to convert

Users unlikely to convert

Data1. App install attribution data: channel,

campaign (input)2. On- and offline customer behaviour event

data: customer service call, app usage clicks and views (input)

Algo1. CLV-model with segments2. Long Short-Term memory time series neural

network

Action1. Optimise media spend based on probability to

drive high CLV2. Drive superb user experience likely to convert

into high CLV users3. Reward high CLV group4. Avoid spamming high CLV users with too

many in-app messages (exclusion)© Avaus

Recipe no: 15

Predict churn risk and automate app retention !


〰〰〰〰

Do you know which of your App users are risking churn?Often companies focus heavily on winback actions or very late stage “panicking” when a customer has already gone too far in the process to change their minds. Predictive churn management and early enough actions enable marketers to increase the effect of their churn prevention actions by up to 5-35%.

Data1. Last login date2. Unique days logged in3. Total time spent4. Features used

Algo1. Churn prediction model(s)

Action1. Identify users at an earlier stage of churning to

increase performance of win-back activitiesa. Identify reasons for churning and

create test hypothesis around them (ex. lack of knowledge on how to use the app can lead to churning)

2. Using insights into each customer’s reason for high churn risk to tailor win-back activities to better effect.

History event data

Churn predictions

DMP export, automated retargeting

How did the already churned customers behave in the history?

Which existing customers have the same behaviour?

What is the impact of our churn prevention actions?

© Avaus

Recipe no: 16

Personalised upsell in chat (live or chatbot)

!〰〰〰〰

Is your chat channel actively optimised for upselling?Often the opportunity of a customer service interaction or a chat is under-utilised for upsell, or if selling on this occasion is tried, it typically has failed due to irrelevant offers. Use algorithms to improve your upsell conversion by 20-80% by giving algorithmic product recommendations.


Data1. Customer data with product history &

possible usage2. Web behaviour data / other engagement data

Algo1. Likelihood to buy, product recommendation

algorithm

Action1. In a manual chat interaction, the customer

service representative can be guided to give product recommendations from an example script

2. In a chatbot interaction, the bot can be given an input for a product recommendation at the end of the interaction automatically, or based on uplift potential

© Avaus

Recipe no: 17

Predict NPS based on service interactions !〰〰〰〰

Are you relying on explicit NPS from your customers?Typically NPS scores are collected manually or automatically after a specific customer interaction, typically with a 30% response rate. By predicting the likely satisfaction of all of your customers based on all textual / voice interaction, you’re capable of creating a better understanding of customer satisfaction and can automate relevant treatment of all, not only the ones who gave you feedback.


Data1. History data on NPS / customer satisfaction

survey results2. Service interactions, Web behaviour data3. If no NPS on individuals - conduct survey

based on customer service interactions

Algo1. (Speech-to-text) for voice2. Sentiment analysis3. Prediction model for NPS score

Action1. Automate targeted actions for different

brackets: Customer service call for customers with predicted low NPS, Surprise benefit for customer with predicted medium NPS at the end of chat interaction, Service messages for customers with predicted high NPS via email

% %

Predicted promoters

Predicteddetractors

Predicted NPS (Net promoter score)

© Avaus

Recipe no: 18

Image recognition in a chatbot to create tailored services !


Data1. Image data input from customers2. Product feed / Content feed (ex. Recipe

content incl. Categories, text and images)

Algo1. Object identification algorithm2. Recommendation algorithm

Action1. Create an automated output feed in the chat

based on the recommendation algorithm:a. Recommendation of a recipe or other

content with a link to read moreb. Recommendation of a product or

service with a link to read more

〰〰〰〰

Are you able to provide your customers with easy digital experiences and light services?Let customers & prospects send in images, or even emojis to your chatbot and provide suggestions on relevant content or product for your chatbot to return (e.g. send picture of your fridge contents and get recipe suggestions from a grocery retailer). This supports up- and cross-sell actions as well as improving customer experience.

Would you like these:

© Avaus

Recipe no: 19

Personalise chatbot interaction with sentiment analysis !〰〰〰〰

Can your chatbot pick up on sentiment?Rules-based chatbots are able to handle most of the default customer service cases. A sentiment analysis-based chatbot is able to make the interaction more engaging and fun by using more sophisticated interaction (text+image) but it also automatically understands when human help is needed - enabling you to reduce cost to serve and improve your customer experience.


Data1. Input + answer text data for chatbot2. Sentiment analysis data (eg. AFINN word list)

Algo1. Long Short-Term memory neural network

Action1. Direct people whose conversations indicate

really negative or positive sentiments from chat bot to human agent

2. Use sentiment analysis to improve product recommendations

3. Use sentiment analysis to modify the chatbot tone of voice

What do you think of this T-shirt?

Glad you liked it. Here’s another suggestion:

Submit answer:

________________

Color, Style, Neckline, Price Color, Style, Neckline, Price © Avaus

Recipe no: 20

Implement a smarter chatbot to reduce cost of service !


Data1. All historically asked questions2. Answers to all historically questions asked

Algo1. Question answering (QA) NLP model to be

able to identify questions posted in natural language.

Action1. Implement the chat to improve customer

experience with quick response rates and improved sophistication of interaction

a. Analyze the results on most important reasons what customers are asking and how the experience is rated

b. Connect the impact on key business metrics (NPS, sales, case handling time)

〰〰〰〰

Is your chatbot able to answer any question?Most chatbots have predefined questions and answers and no ability to understand any question posted by a user. This limits the functionality and the efficiency gains by being unable to answer all questions that customers might have. To truly leverage the power of chatbots you need to be able to process any question posted and with pattern recognition identify which previous questions are really asking the same thing. Implementing an intelligent chatbot can reduce customer service cost by up to 30%.

© Avaus

Biddable paid media21 Use knowledge about existing customers to find new

22 Optimise push notifications for higher CTR23 Optimise in-app actions based on predicted CLV

24 Predict churn risk and automate app retention25 Personalised upsell in chat (live or chatbot)26 Predict NPS based on service interactions

27 Image recognition in a chatbot to create tailored services28 Personalise chatbot interaction with sentiment analysis

29 Identify potential influencers through object identification30 Base your keyword selection on expected CLV

Recipes

Recipe no: 21

Use knowledge about existing customers to find new !〰〰〰〰


Data1. Geographical & behavioural data 2. Transactional data3. 2nd or 3rd party data (e.g. about demographics)

Algo1. Clustering technique: K-means clustering finds

customers with similar attributes that create targetable groups of prospects

Action1. Create of your defined segments as audiences in

your DMP to enable the use in 3rd party channels.

2. Use priority segments in e.g. Facebook lookalike audience for effective campaigns.

Income

Age

Is your media spend not giving the results you want?Lookalike targeting is an effective tool on social media platforms to expand reach and improve results. To make it effective, you need to first define granular homogeneous segments with similar traits and attributes based on what you know about your existing customers. Machine learning can create more granular segments, taking more data points into account than any manual process.

Clustering by using machine learning

© Avaus

Recipe no: 22

Target based on prospect potential to convert !〰〰〰〰

Are you able to target your customer acquisition campaigns to prospects with highest potential?To grow the customer base and decrease the cost of acquisition you can use data based on prospect/lead engagement. By calculating propensity for acquisition it is possible to improve timing and relevance of the acquisition activity, and thus improve cost of acquisition.


Data1. Prospect/lead online behaviour (sites visits

products/services, downloads etc)2. 3rd party (life events, location, interest etc)

Algo1. Propensity model for acquisition

Action1. Differentiate actions & timing based on scores

a. High propensity: Test effect of using only the most cost efficient channels & minimum budget, focus more on content than spending media budget

b. Medium: Test aggressive approach with a boosted media spend to see the effect

c. Low: Exclude from customer acquisition campaigns

Reach Engage Activate Nurture Sell

Calculate propensity

Low

Medium

No action

Triggered activity A

High Triggered activity B

Prop

ensi

ty fo

r ac

quis

ition

© Avaus

Recipe no: 23

Optimise spend of bought media based on uplift potential !〰〰〰〰

Are you using your bought media budget efficiently?Typically the bidding strategies on search ads are aiming at getting a conversion with a set maximum budget or maximising the used budget overall. With algorithmic optimisation we can see significant improvement (+10-50%) on ROI when allocating the budget based on the lift potential of the audience.


Data1. All search advertising history data2. Test and control conversion data (e.g. signups

or upsell conversion)

Algo1. Uplift model to drive a specific conversion

point: identifying an audience which only responds to the action because they were targeted (the segment ‘persuadables’)

2. Different uplift models e.g. based on a product or service

Action1. Target the identified “Persuadables” audience

with search ad aiming to influence them towards the conversion point

2. Exclude the “excess” audience from the targeting to save budget to the higher potentialPr

obab

ility

of p

ositi

ve

resp

onse Prob. redeem

Prob. buy

Uplift

+11%

+37%

A B

+19%

+1%

C DCustomer

© Avaus

Recipe no: 24

Next best offers in bought media for existing customers !〰〰〰〰

Are you using your search ad budget effectively?Traditionally, product recommendations are decided based on the available stock, the business targets or previous product views on the website. These factors don’t take into consideration which product or service the customer is most likely to buy next. Typically, in paid search, the NBO model can bring 10-40% increase in conversion.


Data1. Customers purchase & product history2. Web behaviour / other engagement history (e.g.

from email or app)3. Sources for conversion

Algo1. Predictive models for estimating propensity to

buy specific products2. Next best offer model suggesting best product to

use at the moment in the search ad3. The optimisation can be based on expected

profits, expected change in CLV etcAction

1. Based on the propensities, target the customer with the top 3 products for their next purchase

2. Automate the content creation by connecting the NBO model with your product feed to dig out the top products per customer

© Avaus

Recipe no: 25

Precision re-targeting based on purchase propensity !

Are you efficiently using your budget to re-target people likely to respond to your advertising?Normal e-commerce has around a 1.5-2.5% conversion rate. However most businesses spend money on ad re-targeting on a much bigger proportion of their traffic, perhaps all who have visited their website. Therefore, if we had a 100% accurate model we could save around 97.5% of all money spent on retargeting.


Data1. Web data (Clickstream data) - latest session

and multiple historical sessions2. Conversion history3. Advertising spend data

Algo1. Propensity for individual visitor to buy based

on clickstream2. Potential purchase rate from retargeting (will

marketing increase likelihood to convert)

Action1. Create audiences for high-likelihood segments2. Only spend money of retargeting ads for

customers with a high propensity to buy

〰〰〰〰

High propensity

to buy

Lowpropensity

to buy

© Avaus

Recipe no: 26

Prevent churn with a targeted surprise action !

Do you know how to most efficiently surprise and delight customers at risk of churning?By making use of ‘surprise and delight’ towards customers with high risk of churning you may turn them around. Using this tactic in customer service also adds a personal touch. It can be an add-on service for free, a gift or another action connected to your business. Calculating who is worth spending resources on saving from churn (i.e. who we can prevent from churning) will increase ROI on typically costly churn prevention initiatives.


Data1. Customer engagement and behaviour data

(transactions, products/services, online behaviour etc)

2. Customer churn reasons

Algo1. A predictive model estimating churn risk2. Customer value segmentation3. Build a matrix based on churn risk and value

segmentation identifying most important customer groups to retain

Action1. Create a number of hypotheses about surprise

and delight activities to retain customers2. Test and evaluate effects on retention rate3. Adjust target groups as specific activities

appeal more to typical customer profiles than others

〰〰〰〰

© Avaus

Are you satisfied with your content marketing ROI?The biggest challenge with content marketing is often distribution: how to ensure finding the right audience for our articles, videos and news effectively? How do we produce content that interests our target audience? Content recommendations enable you to use your content distribution budget more effectively while creating great customer experiences.

Recipe no: 27

Create automated content marketing targeting on Social !


Data1. Content marketing assets (meta tags on topic

etc)2. Click-stream data (web behaviour)3. Social media data (1st, 2nd or 3rd party)

Algo1. Recommending the most relevant content (e.g.

top 3-5) per visitor / cookie / identified customer

Action1. Create a facebook carousel ad with the top 3-5

articles most relevant to the receiver, test different images and copy to get the highest impact

2. Instead of static content modules on your social posts, create a module that dynamically populates the most relevant content for the targeted person

〰〰〰〰

© Avaus

Recipe no: 28

Personalise based on psychographic segment !〰〰〰〰

Are you able to be personal with your audiences?Psychographic segmentation is a way to group both prospects and current customers based on their shared attitudes, personality traits, motivations, beliefs, lifestyles and other factors. Basically, psychographic segmentations are used to understand the hearts and minds of the targets and leads to a better connection with the audience and not just reach. Ultimately leading to improved engagement and conversions.


Data1. Research data2. On- and offline customer behaviour.

Algo1. Principal components, hierarchical and

K-means clustering for segmentation.2. Predictive models to roll out research-based

segmentation to customer base.

Action1. Automatically adjust content variants to suit

target groups’ psychographic profile ex.a. Toning the message based on

personalityb. Content variations based on customers

values or interest (ex. sustainability or family)

Personality

Values

Interests

Lifestyle

Age

Gender

Location

Employment

Psychographics Demographics

© Avaus

Are you struggling to efficiently identify new influencers for your marketing campaigns?Scan through thousands or millions of user generated content on social media while collecting key data points about your customers posting about your products and/or brand. Use this method to identify potential key influencers to connect with and give special treatment.

Recipe no: 29

Identify potential influencers through object identification !


Data1. User generated content on social media2. Profile data on users posting

a. Amount of followersb. Amount of posts & consistency

3. Interactions data on posts relevant to your brand

a. Likes & Comment

Algo1. Object identification to identify branded

products2. Identify users driving engagement for your

brand/products(s)

Action1. Contact potential influencers to become

ambassadors for your brands2. Make targeted brand ambassador

communication to further engage with the potential brand advocated

〰〰〰〰

© Avaus

Have you considered optimising your search budget against expected customer lifetime value?Search engine marketing is competitive and many businesses struggle with their return on investment on bought keywords. Discovering the keywords that lead to acquiring high-value customers increases ROAS and informs better bidding strategies.

Recipe no: 30

Base your keyword selection on expected CLV !


Data1. A sufficient range of historical keyword

acquisition data paired with customer data2. Customer data such as purchasing history

and/or other engagement metrics

Algo1. Predictive model to estimate CLV (customer

lifetime value) for each customer 2. Paired with historical keyword data

Action1. Budget model based on CLV and historical

keywords2. Optimised keyword selection based on CLV

(with the potential to discover underutilised keywords). Or if you are a niche bank or an insurance company – why not also mitigate adverse selection?

〰〰〰〰

Quick loan

Low interest rate

Refurbish kitchen

Tesla Model S

Summer house Nordkoster

Hemnet Mosebacke

Boat stockholm Blocket

Delay tax payment

Car Blocket malmö

© Avaus

Call centre /Customer service

31 Next best offer (NBO) for customer service32 Algorithmic quality assessment of service in social media

33 Tailored sales calls based on recipient personality34 Personalised coach for call center agents based on speech data

35 Reduce average handling time

Recipes

Recipe no: 31

Next Best Offer (NBO) for customer service

!

Are you supporting your customer service with analytical models that suggest the next best offer?Seize the moment of opportunity when the customer is open to discuss your products and services. A machine learning-based NBO model proposes offers based on data and predictive models and also takes into account other factors (such as recent engagement data). Sometimes the next best offer is no offer at all!


Data1. Customer engagement data (buying patterns,

services used etc)2. Customer behaviour data (online activity,

issues via customer service etc)3. Past outbound actions and results

Algo1. Predictive models for x-sell/upsell of

products/services that are part of the NBO model

2. Apply rules for business and communication 3. Optimisation model finding Next Best Offer

Action1. Show top 3 NBO in customer service interface2. Tailor the communication about offers based

on current conversation and NBO3. Report for follow-up and results of NBO

recommendations

〰〰〰〰

© Avaus

Recipe no: 32

Algorithmic quality assessment of service in social media !


Data1. Text-based responses in social media from

customer service reps in all relevant channels2. Ability to individually identify the reps

(back-end)

Algo1. NLP-based model to assess quality on each

customer service rep response

Action1. Daily tracking for managers on service

response quality2. Insights such as channel specific service

response quality per rep, as a base for reallocation of resourcing, prioritisation etc.

3. Automated weekly report for each sales rep with tailored recommendations on tonality, wording and cherry-picked examples

〰〰〰〰

Do you know how you and your representatives are performing in social media customer service?Customer service representatives today are expected to serve and reply to huge amounts of customer interactions and complaints on social media. For a manager, it’s hard to evaluate, and at same time give valuable feedback to every single rep. In the current service landscape, service quality is often insufficiently evaluated through ongoing surveys. Instead, use algorithms to guide you.

© Avaus

Recipe no: 33

Tailor sales calls based on recipient personality !


Data1. Social media profile data2. Other 2nd and 3rd party data relevant

(comments, likes, interests)3. Voice data from call (real-time)

Algo1. Algorithm segmenting target personas based

on predicted psychographic profile

Action1. Automate the giving of real-time suggestions for

sales agent to tailor communication style for increased conversion likelihood

2. For example tailoring the sales calls content & agenda based on the personality (e.g. a person who is willing to take risks, wants to move fast to goal setting and action plan is different from a comfort seeking person)

〰〰〰〰

Are you able to tailor your sales calls to fit the recipient’s personality?Understanding the psychographic profile of a prospect enables you to tailor communication according to personality type. Addressing a potential prospect based on their personality type can increase the hit rate by up to 30%. It also improves conversions across the buying journey.

Personality

Values

Interests

Lifestyle

Approach

BA

B

A

A

B

C

C

D

D

D

A

E

G

F

F

E

C

C

A

B

B

E

E B

A

A

C

G

F

F

G

X Y Y W Z V Z X© Avaus

Recipe no: 34

Personalised coach for call centre agents based on speech data !

Are you able to clone your best agents’ behaviour?Typically customer service & call agents get a general training and education on how to handle calls and perform effectively. Machine learning helps to identify patterns which high performing agents use and can give insights on the individual improvement areas (e.g. customer satisfaction, sales proficiency, first call resolution, problem solving skills etc) in real-time - to replicate what works and give every agent the ability to be a top-performer.


Data1. Call centre or customer service calls in text or

voice format, and conversions connected to the calls

2. KPIs for measuring efficiency and performance

Algo1. Speech to text (STT) and natural language

processing to identify patterns between high-performing call centre agents and phrases/tactics used

2. Live STT during calls, analysis of performance risks

Action1. Automated “call success” - traffic lights on e.g.

customer likelihood to buy / NPS2. Automated “call coach” on recommended

resolutions / pitches / sentiment direction3. Individual agent analysis reporting and

coaching possibilities

〰〰〰〰

Data+Algo checked/H

✔This one’s OK from Emma

© Avaus

Recipe no: 35

Reduce average handling time

!

Are you optimising resolution / handling time with data?Ensuring fast / first call resolution and decreasing the average handling time of customer service calls means big bucks for most companies. By using machine learning call centres can improve their efficiency by automating decisions, providing the right resolutions immediately and routing the calls to the right place in the first place.


Data1. Call centre or customer service calls in text or

voice format and conversions connected to the calls

2. Live calls / IVR data

Algo1. Speech to text algorithms (if calls)2. NLP to extract information on issues3. Predictive models to understand likelihood of

success with different automated actions

Action1. Prompt URLs for self-resolution while waiting2. Route calls to right specialist based on

customer data and/or problem description3. Bring up resolution suggestions for call agents

based on issues captured from speech

〰〰〰〰

x

x x

x x x

x x

Data+Algo checked/Ola

✔This one’s OK from Emma

© Avaus

B2B Sales & Marketing36 Predict need for maintenance and automate sales

37 Predictive lead / prospect scoring38 Analytical scoring of deal quality

39 Accelerate pipeline velocity / win-rate40 Dynamic pricing for maximise profit

41 Predict buying window and product purchase propensity42 Capacity and sales optimisation

Recipes

Recipe no: 36

Predict need for maintenance and automate sales !


〰〰〰〰Data

1. Product usage data (all customers)2. History of services provided per product (all

customers)3. Customer data and current products

Algo1. Predictive model for future service needs

Action1. Based on the algo output use push channels

(email, sms, app push) to proactively contact the customers who need a service visit within the next 3 months.

2. Sending a reminder in other channels to customers who have not booked a service appointment

3. Tailor customer portal interface to suggest the service appointment the next time the customer logs on

Optimal time to service

Now 3 months

Are you proactively managing service costs?Instead of waiting for the customer to contact you about their solution needing maintenance, you can predict the optimal time to service a specific piece of equipment to prevent it from breaking down. This creates a better customer experience, reduces service costs (primarily through minimised down-time) and creates a lock-in effect as you can provide significantly more effective service than 3rd party competitors.

© Avaus

Recipe no: 37

Predictive lead and prospect scoring to identify high potential !


〰〰〰〰

Is your lead scoring model learning?With the proliferation of marketing automation lead scoring has become commonplace. But the rules-based lead scoring models of these platforms only go so far. Predictive scoring of leads, prospects, (and why not existing clients?), allows you to add the statistical validity that is so often missing, increase the number of data points that are taken into account, and speed up the pace of learning necessary to increase the accuracy of your model.

Data1. History of products/services provided per

account (all customers)2. Account & contact data and current

products/services3. Customers digital footprint

Algo1. Predictive lead scoring model

Action1. Based on the algo output rank leads/prospects

in order of priority2. Distribute your sales and marketing resources

accordingly3. Automatically target and trigger ad and MA

campaigns according to scores

+3 +10 +5 +2 +2 +3 +10 +15 +5© Avaus

Recipe no: 38

Predictive scoring to identify quality deal

!


〰〰〰〰

Do you know what a good deal looks like?It is complex for sales management to assess the quality of a deal at face value, given the complexity in pricing and scope of especially more complex deals. With predictive deal scoring we draw upon all the deals, cluster similar ones, thus allowing us to tell the quality of a proposed deal.

Data1. Proposal and contract data (all customers)2. Pricing information3. Profitability data per account4. Customer data and current products

Algo1. Clustering algo to identify similar deals for

benchmarking2. Predictive model for profitability of deal

Action1. Connect algo to CPQ to provide instant

feedback on deals being configured - Am I pricing above or below average?

2. Connect to predictive lead scoring and/or predictive CLV/churn models to balance pricing decisions against key factors affecting profitability

?

Sales representative

© Avaus

Recipe no: 39

Accelerate sales pipeline velocity and win-rate !


〰〰〰〰Data

1. CRM (Account & contact data, current products/services, opportunity, closing rate)

2. Transaction history3. Marketing campaign data4. Sales metrics (pipeline data)

Algo1. Attribution model (marketing impact on sales)2. Uplift model (likelihood that marketing will

influence outcome of opportunity win/loss)

Action1. Automated ABM campaigns, email campaigns

and / or website personalisation2. Alert marketing department to align with sales

for non-automated support (e.g. event invitations, hospitality etc)

Which deals you should support with marketing?It has been proven that marketing likely will increase your close-rate, deal size and pipeline velocity. Knowing which opportunities you should influence by calculating uplift potential from marketing activities should inform your automated marketing campaigns and be triggered when a positive outcome on sales metrics is predicted.

© Avaus

Recipe no: 40

Dynamic pricing for maximised profit of all sales cases !


〰〰〰〰

Are you able to base pricing on capacity and demand?Being able to have flexible prices and base them on demand, internal capabilities, restrictions, ability to deliver as well as the customers’ willingness to pay will allow you to tap into profitability gains that are hidden by fixed or rules-based pricing. Revenue can be increased both by being able to charge a higher price when possible, and also to sell more when the willingness to pay is below the static price.

Data1. CRM (opportunity, account etc)2. ERP/transactions and other price points3. Behavioural data ex website engagement

Algo1. Self-learning algorithm such as reinforcement

learning based on historical data

Action1. Provide configurators on website and/or

eCommerce2. Provide calculators on website and/or

eCommerce3. Trial signup to test propensity to convert to

purchase4. Connect algo to CPQ to provide instant

feedback on deals being configured - Am I pricing above or below average?revenue

DEMAND

p1

STATIC PRICING

revenue

p1

p2

p3

DEMAND

extra revenue

DYNAMIC PRICING

© Avaus

Recipe no: 41

Predict buying window and product purchase propensity !


〰〰〰〰

Do you know when your customer will buy next?Especially for transactional and volume-based B2B businesses, predictive modelling of when and what a customer will buy next will help B2B sales managers to automate sales processes, as well as put sales focus on the right deals. With Marketing Automation and ABM connected to propensity scoring, creating an automatic sales-machine is not far ahead.

Data1. Transactional data2. 3rd party data (e.g. firmographics, growth)3. Engagement data (web, email) incl metadata

(e.g. what content has been consumed online)

Algo1. Likelihood to buy / Buying window2. Next Best Product / Offer (likelihood to buy

different products)

Action1. Automate outbound sales activities (sales reps

tasks in CRM or marketing campaigns)2. Personalise campaigns with the right product /

solution3. Give sales rep input on what is most likely to

be relevantBuying window 6 months

© Avaus

Recipe no: 42

Capacity and sales optimisation to increase bottom line !


〰〰〰〰

Selling 100% of your output? OK! Are you selling to the right customers?It is a normal setting in the B2B scene. You’re selling close to your entire output, so generating new business leads isn’t at the top of your list. In this situation, the route to improved business results is by getting smart about who you sell to. And even if you aren’t shifting your entire output at the moment, it makes sense to balance between equipment utilisation, high-margin deals (often more volatile) and lower risk deals (often lower-margin agreement customers).

Data1. Product usage data (all customers)2. Production or service capacity data (typically

tied to time and location)3. CLV & Churn propensity data

Algo1. Regression model gauging price elasticity and

propensity to buy for each account2. Optimisation model for optimal capacity

allocation with respect to goal variables (e.g. Churn risk and Revenue or Profitability, i.e. CLV). Output prioritised ranking of accounts.

Action1. Based on the algo output give priority to

highest ranked accounts when negotiating for available output over coming years

2. Potentially renegotiate low-ranking (unprofitable) accounts & reallocate capacityvsSales

© Avaus

Capacity

Omnichannel / Customer strategy43 Predict major life events: Moving, retirement or other

44 Create better audiences with response modelling45 Use your budget better with marketing mix modelling

46 Multi-touch attribution model for omnichannel orchestration47 Next best action for omnichannel orchestration

48 Content sequencing to create an optimal customer journey49 Churn risk classification and “vaccine” actions

50 Product library categorisation

Recipes

Recipe no: 43

Predict Major Life Events: Moving, retirement or other !


Data1. Customer demographics2. Offline behavior3. Online browsing behavior4. Past purchases

Algo1. Predictive analytics to estimate likelihood of

customers undergoing specific life events

Action1. Trigger specific life-event journeys where

relevant (e.g. in email / paid social)2. Present relevant content, inspirational/sales

based messaging to capitalise on changed buying patterns

〰〰〰〰

Are you aware of your customers major life events?Through predictive analytics, marketers can identify major life events that are relevant to your business. This is important due to the change in behaviour which in turn is an opportunity to both serve the customer relevant content & capitalise on changed buying patterns. An example would be predicting when a customer is likely to move or start a family.

History FuturePresent

© Avaus

Recipe no: 44

Create better audiences with response modelling !


Data1. Customer behaviour data ( transactions and

online behaviour) 2. Response data (what treatment did the

customer get/respond to)3. Product/service data (margin on products sold)

Algo1. A predictive model estimating the uplift of a

marketing activity

Action1. Exclude audiences with expected low response2. Target actions to audiences with high potential

to e.g. upsell message via app push3. Create dashboards for follow-up on each

action and success rate of each algo (effect of target group - effect of control group).

〰〰〰〰

Are you marketing to audiences who won’t react?What is the likelihood of a marketing action leading to the expected outcome? For example, what is the probability that a churn prevention action will actually lead to that churn being prevented? Boost the success rates of all marketing actions, increase customer experience via effective targeting and get insights for monitoring.

Insights of treatment program

Customer state

Churn treatment A

X-sell treatment A

X-sell treatment Betc...

Propensity models

Segment

Customer preferences

Altered customer state

© Avaus

Recipe no: 45

Use your budget better with marketing mix modelling !

Do you have the insights to make informed decisions on how to allocate your media budget efficiently?CMOs often lack a coherent overall view which would make all the different channels comparable. Media mix modelling can optimise media spend by up to 25% by recommending ideal campaign periods, budget/media pressure and the right media mix. It is particularly useful to understanding the impact of traditional media such as broadcast TV.


Data1. Advertising campaigns (across ad channels

(online & offline), timing, content, results, budget etc)

2. Other marketing campaigns (across own channels, timing, content, results, budget etc)

3. Sales performance data4. Other data affecting results (market changes,

weather etc)

Algo1. Media mix model (using regression models)

against a set goal / KPI (typically revenue/sales)

Action1. Based on the output recommendations,

optimise:a. Campaign timingsb. Budget per mediac. Optimal mix of media

〰〰〰〰

Consumer response

Business outcomes

Market conditions

1Attri-

bution

2Optim-isation

3Alloca-

tion

Market activities

Competitor activities© Avaus

Recipe no: 46

Multi-touch attribution model for omnichannel orchestration !

Are you giving fair credit to each media channel or customer touchpoint?Typically companies are using an attribution model only for paid advertising and utilising ready models, for example provided by Google Analytics. These models are typically rather simple (first click, last click, time decay etc) and only consider a limited number of channels. With a more sophisticated attribution model using machine learning, ROI can improve 5-25%.


Data1. Interaction data across channels (app, web,

email, customer service, advertisement, refunds, cross-device, offline orders etc)

Algo1. Algorithm using Shapley values (game theory)

giving a fair credit to each touchpoint and the sequence of the channel.

Action1. Use the results of the algorithm to optimise

the current and future marketing campaigns by testing different variations of

a. Contentb. Channelsc. Sequencingd. Timing

〰〰〰〰

Giving fair credit to each player

APaid search BDisplay CSocial

© Avaus

Recipe no: 47

Next Best Action for omnichannel orchestration !

What would be the next best action for every single customer – at a given point in time?Next best action is about moving from channel specific personalisations into the centralisation of personalisation with a large number of data attributes. This gives more relevant and unified digital experience across channels. Revenues are increased through the right offers reaching the right audiences at the right time.


Data1. Customer behaviour data (transactions,

channels, online activity etc)2. Product/service data (margins, channel

specific, business rules etc)3. Results of actions on individual level

initialised by company

Algo1. Customer segmentation model(s) 2. Predictive models for upsell/x-sell for

products/services within NBA, churn risk, channel preference etc.

3. Optimisation model to determine the purpose for next action and next best action

Action1. Unified actions across channels. The same

communication will reach the customer independent of channel.

〰〰〰〰

A

SELL

SERVE

RETAIN

Customer needs

Business objectives

NEXT BEST ACTION

DC

W Z

YX

B

© Avaus

Recipe no: 48

Content sequencing to create an optimal customer journey !


Data1. Engaged content before purchase2. Content classified in relevant groups

(inspirational/product driven etc)3. Time to purchase

Algo1. Predictive model suggesting what type of

messaging should be served next to each customer

Action1. Producing a content library with pieces tagged

with inspirational or product messaging to enable automatic usage of the optimal content sequencing (ex. 3 pieces of inspiration and 2 pieces of product driven messaging to convert users during an upsell campaign leveraged through display)

〰〰〰〰

Should your next content piece be inspirational or product driven?By analysing our customer journeys and what content was consumed on the path to a purchase we can identify the optimal amount of inspirational vs. product-driven messaging that was needed to convert a user. Using this knowledge we can apply this to any new user and serve relevant content to support them on their individual buying journeys. We can also use this information to find out the optimal timing by looking at the time to purchase.

© Avaus

Recipe no: 49

Churn risk classification and “vaccine” actions !


Data1. Transactional data2. Customer demographics3. Customer online behaviour4. (Sentiment / NPS / Claims data)

Algo1. Predictive model - Churn prediction (when)2. Shapley values for understanding reasons3. Possible: Treatment effect likelihood

Action1. Triggered vaccine treatment for early churn risk,

active “win-back” for high risk 2. Individualised treatment based on churn reason 3. Spend and channel selection based on retention

/ churn value and ability to influence retention (vs “lost cause”)

〰〰〰〰

Do you know which action to take for at-risk churn?When predicting churn and aiming to prevent it, it is not enough that we know who might be churning. We also need to understand the high-level reasons for churn as well as what contributed to each individual’s churn risk - so that we can act on it with the right action. An additional level of sophistication is to calculate whether a churning customer can be retained, and is worth spending on.

What causes churn?

What is the value of retention?

What is behind the individual’s churn risk?

Who can be affected?

© Avaus

Recipe no: 50

Product library categorisation

!

Are you using machine learning to help categorising your large product library?For any company managing thousands or even millions of products, manually assigning to which categories a product belongs is a very time-consuming process. Getting this right though is very important, since many business and marketing decisions are based on product categorisation and hierarchies.


Data1. Product metadata2. Transactional data

Algo1. Substitutability/market basket algorithm to

understand product substitutability2. Clustering algorithm to group similar

products3. Decision tree to create hierarchy, based on

product attributes

Action1. Use categorisation to navigate through

products in e-commerce2. Input as an ongoing process to minimise time

spent on categorisation

*Good thought example is Amazon, managing hundreds of millions of products is not possible without the help of ML

〰〰〰〰

© Avaus

Table of Contents

Foreword…………….....………………..1

Equipping your kitchen...............2

Recipes…………….……….………….....3

Templates…….…….………….....4 Join the #algoleap..………….………..5

Get going on your data-algo-action journey!

PlanGather your team and plan end-to-end use cases with the use case planning template. Remember that the “algo” part is only 10%-20% of the job and make sure you have considered the complexity of especially the “action” element of your use case.

Create a backlog of all your ideas, and use the ICE backlog prioritisation framework to define which use cases to implement first!

Execute!

When executing, you might need access to more data sources than previously - use the data requirements template to specify to your IT team what you need

Analyse and Report

When you have received the first results of your end-to-end use case, collect learning and results for your team, but also for the entire organisation to learn - for this you should use the PoC Report out template

When reporting results to the c-level / business owner, ensure you are speaking in business results - for this you can tweak the Business impact report © Avaus

[Data] x [Algo] x [Action]

End-to-end use case planning template

What data is needed for this use case?

And in more detail: Do we have this data today?If not, how to collect it?If yes, how to access it?

What are we trying to predict with the help of the data?

And in more detail: How should the algorithm output appear to fit to the [action]?What kind of analytical approach should be taken?Where / how to create the algorithm? How / where to operationalise?

How will we activate the algo output to achieve our business objective?

And in more detail: What channels will we activate in?What processes will use the algorithm output as input values?What do we need to build in terms of e.g. campaigns, processes etc for activation?

[Data] [Algo] [Action]

Measurement strategy

What are the KPIs to be followed? How will we be able to measure the effect? Compared to status quo / other activation?

Risks / Dependencies

What risks are involved in delivering the end-to-end use case? How can they be mitigated?What are the key dependencies for the use case to be implemented?

Business Objective

What business problem are we trying to solve?

Download the template:

avaus.com/cookbook!

© Avaus

https://www.avaus.com/cookbook

[Impact] [Confidence] [Ease]

How big an impact can this idea have on the overall goal?

Scale:1 = Small impact (tiny tweak)10 = Big impact ($$, new business area / product line)

How certain are we of the success of this solution?

Scale:1 = Very risky project (probability close to 0%)10 = Completely safe (probability of 99%)

How easy is this solution to develop?

Scale:1 = Very difficult (Long development, high resourcing)10 = Super simple (quick and easy)


ICE backlog prioritisation framework

Backlog item Short description of the backlog item

= ICE score (Sum of score): 3-30

1-10 1-10 1-10

This is a framework you can use to prioritise your backlog of data-algo-actions and determine from where to start.


avaus.com/cookbook!

© Avaus



Data requirement template

Business Objective Why are we collecting this type of data?

Business use cases

What are the top 3 use cases for using this data?

● Use case 1● Use case 2● Use case 3

Business impact

Expected business impact?● Use case 1● Use case 2● Use case 3

Data specifications

What are the key requirements for the data?● Format● Specific tables● Columns required● Update frequency/latency● History needed● System ● ...

Use this template when talking to your IT / data team about getting access to new types of data (e.g. click-stream data)


avaus.com/cookbook!

© Avaus



PoC Reporting template

●

●

Key insights & learnings

●

Recommendations for the future

Case description

Business unit:

Product:

Idea:

Target group:

Expected benefits / key KPIs:

Business potentialCustomer experience

Proof of concept test results

Case rating

Business case: Incremental annual revenue


avaus.com/cookbook!

© Avaus


● How much incremental revenue have we been able to generate?

● What is the baseline and how much on top of that can we improve the results?


Business impact report

Conversion rate Incremental revenue Incremental profit

These are the key Business KPIs you should be reporting on your data-algo-actions to get traction from the rest of the organisation

+X % YoY OR vs Control

+X € / SEK YoY OR vs Control

+X % YoY OR vs Control

● How much did the conversion rates improve over time?

● Are we heading to the right direction month-by-month?

● What is the target for the next quarter?

● How much more revenue are we driving from the data-algo-action than before?

● What is the return of our investments now compared to before?


avaus.com/cookbook!

© Avaus


Table of Contents

Foreword……………...………………….1

Equipping your kitchen..............2

Recipes…………….…………………......3

Templates…….………………………....4

Join the #algoleap..……………..5

Share your feedback and your own data-algo-action recipes with us on LinkedIn by mentioning @Avaus and using #algoleap

Excited, but need help to get started? Don’t hesitate to contact us via email: [email protected]

Want to join our team and learn how to turn data into results with the use of AI and algorithms? See our open positions and contact us here: avaus.com/careers

We challenge you to take the Algo Leap with Avaus!

https://www.avaus.com/careers/

Thank you for reading!

First Edition, October, 2019Copyright © 2019 Avaus. All Rights Reserved.

This is a print of the living document. We’re updating the content as we go. Keep your team up-to-date & get access to the online version here: avaus.com/cookbook

www.avaus.com

Intelligent Growth.

http://www.avaus.com

AI in Marketing & Sales - Avausinfo.avaus.fi/rs/878-EHZ-827/images/Avaus_Cookbook_oct_2019.pdf ·...

Documents

Transcript of AI in Marketing & Sales - Avausinfo.avaus.fi/rs/878-EHZ-827/images/Avaus_Cookbook_oct_2019.pdf ·...