<#>© 2014 LUMATA All Rights ReservedWhite Paper l Capturing & Converting Multi-SIM Users 1White Paper l Capturing & Converting Multi-SIM Users © 2014 LUMATA All Rights Reserved

W H I T E P A P E R

M A R K E T I N G

From understanding behaviour to increasing revenue: 4 key steps

White Paper l From understanding behaviour to increasing revenue: 4 key steps2

Driving customer retention & ARPU in prepaid markets

Increasing churn rates are a real concern for operators and particularly in countries whereprepaid, low ARPU customers and multi-SIM users are prevalent.

Many operators rely on below-the-line (BTL) mobile marketing campaigns to try and reach their prepaid customer basewith messages and incentives aimed at increasing retention and stimulating revenues.

In reality, due to the frequent lack of resources , experience and IT tools in the area of analytics and marketingautomation, such campaigns traditionally tend to be highly tactical, unpersonalised and often out of context, rather thantied into a coherent marketing strategy across the customer base. For instance, disparate campaign offers focusing ontariff competition and customer acquisition quick fixes, instead of accurately addressing the true needs of customers overtheir entire lifecycle, usually actually create the opposite effect to what was originally intended. Operators end up withrapid cannibalisation of revenues, eroding profits and churn.

In the face of ever growing competition, new entrants and decreasing margins, it is now critical for operators’ marketingteams to get the basics right, and only a deep understanding of the customer behaviour and a holistic campaign planningexercise can enable them to reach their goals.

In this white paper, we share best practice on delivering behavioural analysis and the implementation of successful contextualmarketing strategies and techniques that support operators’ objectives around retention and revenue development.

Innovating from your data

Micro offers: An operator was segmenting theircustomers by ARPU and had tried many upsell strategieswith little success and a high level of revenuecannibalisation. Through clustering, a cash-consciousgroup of customers was identified through their uniquebehaviour within this segment and a new strategy wasdeployed leveraging micro offers (time-limited, low-pricedPAYG offers) which were very successful in increasingusage and spend.

Personal on-demand offers: Operators struggle toretain customers in highly competitive markets. Throughan understanding of the behaviour of the churners, a newretention strategy was deployed to enable customers torequest at their own convenience a personalised offertailored to their needs via SMS or USSD. The strategy

enabled the operator to save customers at risk of churnwithout destroying value in the rest of their base.

Next-generation loyalty: An operator with a history of points-based loyalty programmes had been strugglingto retain customers and build engagement. By analysingcustomer behaviour, it became clear that their definitionof loyalty had to change in order to better targetincentives by segment. Simple metrics, like spend, failed to identify the true value of a customer. We built a multi-dimensional clustering model that redefined ‘loyalty’ in terms of spend, tenure, social advocacy, digitalengagement and other relevant variables. The clusters thatemerged, helped us redefine the reward types, frequencyand engagement plan. Today, the operator is able to be farmore relevant and personal in driving retention initiatives.

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characteristics. Analysts are looking for key differentiatingvariables to accurately understand the behaviour andneeds of the cluster, identify marketing opportunities forthe operator and create the recommendations that will beturned into offers and marketing campaigns.

A predictive engine that is optimised for each clusterenables the operator to reach their base with automatedcampaigns that maximise relevance and minimiserevenue cannibalisation.

Different customer segments churn for different reasons.Looking at churn data for each cluster over a period ofseveral months enables to understand which clusters arechurning more and why, as well as define revenuedevelopment opportunities for each of these clusters.

Once the operator understands the customer behaviour,they can next endeavour to influence it by implementingthe right strategies as part of an ongoing process. It is key to build a holistic customer journey strategy bycreating multi-step campaigns that adapt in real time to the reaction of the customer once a marketingprogramme is live.

STEP FOUR

Automating insight to revenue

The success of a BTL mobile marketing strategy ultimatelylies in the ability to deliver highly personalised offers to theright customer, at the right time.

Taking the cluster information and recommended campaignmodels into a real-time mobile marketing software productthat can define the context through which the cluster iscontacted will maximise the conversion rates and enable the operator’s marketing team to automate the campaigns.

In order to minimise revenue cannibalisation, a campaignrecommendation engine will be trained to predict the bestoffer for each cluster based on their particular behaviourand the portfolio of offers available.

Data analytics and marketing consultancy enable theoperator to understand the customer base and define a strategy to develop it. Through real-time self-learningpredictive marketing software, the operator can thenautomate the implementation and optimisation of this strategy.

Developed through our long-running experience in providingconsultancy services to operators across emerging marketsas well as in developed economies, our methodology forturning data into actionable insight involves four key steps:

STEP ONE

Forming a holistic customer view

The purpose of this step is to validate the operator’s vast amounts of customer information into a single view in orderto understand the patterns within the base. Analysts first perform a thorough data cleansing exercise followed by theaggregation of data existing in different systems from theoperator. Finally, data normalisation will enable them to understand the emerging patterns within the customer base.

Advanced analytics capabilities are an absolute requirementat this stage to filter out the noise in the data and focus onthe key behavioural patterns.

STEP TWO

Behavioural segmentation

Using a variety of statistical methods, the analysts nextidentify like-minded customers and map out their uniquebehaviours, in order to subsequently derive logicalmarketing clusters that are based on the customers’ needsand aligned with the marketing strategy of the operator.

We use all of the data available within the operator’s system,from transactional CDRs to provisioning, device and socio-demographics. A customers’ relationship with their operatoris more than just their spend so a broad spectrum of factorsare taken into account wherever the data is available.

To be able to form natural groupings within the customerbase, what is required is a combination of science, humaninference and diligent reiteration of the data mining andanalysis process.

STEP THREE

From profiles to journeys

A robust analysis of the clusters is performed (a clustermay contain 300 data points!) in order to bring out key

© 2014 LUMATA All Rights Reserved

4© 2014 LUMATA All Rights ReservedWhite Paper l From understanding behaviour to increasing revenue: 4 key steps

L U M A T A . C O M

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contact@lumatagroup.com

Contact us

Contact our team of experts at Lumata to find out how we can help you turn your data into revenue.Call us now on +44 (0)20 7613 8900 or email contact@lumatagroup.com.

Lumata is a global software and marketing services companyfocused solely on the mobile marketing space. Our goal is to provide operators, brands and advertisers with thetools they need to manage their brand’s interaction withconsumers over mobile. Our 10-year heritage and successis built on a strong foundation of market-leadingtechnology, deep category expertise and a focus ontransparency and measurement. Lumata is backed byFrancisco Partners, a leading technology focused privateequity firm with over $7Bn under management.

Challenges:

An operator based in Europe was faced with:

• Low average reach rate (20%) and response rate (12%).

• Highly competitive market: aggressive pricing tactics, multi-SIM users.

• Traditional approach to mobile marketing: segmenting by ARPU and tenure.

• ‘One-size-fits-all’ marketing strategy: pushing the same upsell packs to all customers and communicating with all customers at the same time with little effect.

Solution:

By understanding the underlying customer behaviour andgaining insight into how the customer interacts with thebrand, it became clear that not all customers in a specific ARPU segment had the same needs. Customers with a clearpreference for text were previously offered voice offers,

whilst customers with a smartphone were offered data when they were only interested in calls and texts. Thanks to the behavioural segmentation, the operator was able to identify the relevant strategy for each cluster and developed contextual customer journeys through multi-step campaigns.

Results: Campaign Performance

• Improved reach rate (70%) and response rate (17%).

• Lower to mid-spend customers achieved an average revenue uplift of 4%.

CASE STUDY

Driving retention and revenue growth with behavioural insight

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