White Paper l Capturing & Converting Multi-SIM Users

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M A R K E T I N G

© 2018 EVOLVING SYSTEMTS All Rights Reserved

From understanding behavior to increasing revenue: 4 key steps: MARKETING

Driving customer retention & ARPU in prepaid markets

Innovating from your data

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

Many operators rely on below-the-line (BTL) mobile marketing campaigns to try to reach their prepaid customer base with 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 marketing automation, such campaigns traditionally tend to be highly tactical, personalized and often out of context, rather than tied into a coherent marketing strategy across the customer base. For instance, the disparate campaign offers to focus on tariff competition and customer acquisition quick fixes, instead of accurately addressing the true needs of customers over their entire lifecycle, usually actually create the opposite effect to what was originally intended. Operators end up with rapid cannibalization of revenues, eroding profits and churn.

In the face of ever-growing competition, new entrants and decreasing margins, it is now critical for operators' marketing teams to get the basics right, and only a deep understanding of the customer behavior and a holistic campaign planning exercize can enable them to reach their goals.

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

White Paper l From understanding behaviour to increasing revenue: 4 key steps © 2018 EVOLVING SYSTEMS All Rights Reserved 2

Micro offers: An operator was segmenting their customers by ARPU and had tried many upsell strategies with little success and a high level of revenue cannibalization. Through clustering, a cash-conscious group of customers was identified through their unique behavior within this segment and a new strategy was deployed leveraging micro offers (time-limited, low-priced PAYG offers) which were very successful in increasing usage and spend.

Personal on-demand offers: Operators struggle to retain customers in highly competitive markets. Through an understanding of the behavior of the churners, a new retention strategy was deployed to enable customers to request at their own convenience a personalized offer tailored to their needs via SMS or USSD. The strategy enabled the operator to save customers at risk of churn without destroying value in the rest of their base.

Next-generation loyalty: An operator with a history of points-based loyalty programs had been struggling to retain customers and build engagement. By analyzing customer behavior, it became clear that their definition of loyalty had to change in order to better target incentives 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, digital engagement and other relevant variables. The clusters that emerged helped us redefine the reward types, frequency and engagement plan. Today, the operator is able to be far more relevant and personal in driving retention initiatives.

Developed through our long-running experience in providing consultancy services to operators across emerging markets as well as in developed economies, our methodology for turning 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 order to understand the patterns within the base. Analysts first perform a thorough data cleansing exercise followed by the aggregation of data existing in different systems from the operator. Finally, data normalization will enable them to understand the emerging patterns within the customer base.

Advanced analytics capabilities are an absolute requirement at this stage to filter out the noise in the data and focus on the key behavioral patterns.

STEP TWO Behavioral segmentation

Using a variety of statistical methods, the analysts next identify like-minded customers and map out their unique behaviors, in order to subsequently derive logical marketing clusters that are based on the customers' needs and 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 operator is more than just their spend, so a broad spectrum of factors are taken into account wherever the data is available.

To be able to form natural groupings within the customer base, what is required is a combination of science, human inference and diligent reiteration of the data mining and analysis process.

STEP THREE From profiles to journeys

A robust analysis of the clusters is performed (a cluster may contain 300 data points!) in order to bring out key variables to accurately understand the behavior and needs of the cluster, identify marketing opportunities for the operator and create the recommendations that will be turned into offers and marketing campaigns.

White Paper l From understanding behaviour to increasing revenue: 4 key steps © 2018 EVOLVING SYSTEMS All Rights Reserved 3

A predictive engine that is optimized for each cluster enables the operator to reach their base with automated campaigns that maximize relevance and minimize revenue cannibalization.

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

Once the operator understands the customer behavior, they can next endeavor to influence it by implementing the right strategies as part of an ongoing process. It is a key to build a holistic customer journey strategy by creating multi-step campaigns that adapt in real time to the reaction of the customer once a marketing program is live.

STEP FOUR Automating insight to revenue

The success of a BTL mobile marketing strategy ultimately lies in the ability to deliver highly personalized offers to the right customer, at the right time.

Taking the cluster information and recommended campaign models into a real-time mobile marketing software product that can define the context through which the cluster is contacted will maximize the conversion rates and enable the operator's marketing team to automate the campaigns.

In order to minimize revenue cannibalization, a campaign recommendation engine will be trained to predict the best offer for each cluster based on their particular behavior and the portfolio of offers available.

Data analytics and marketing consultancy enable the operator to understand the customer base and define a strategy to develop it. Through real-time self-learning predictive marketing software, the operator can then automate the implementation and optimization of this strategy.

Results: Campaign Performance

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Contact us

E V O L V I N G . C O M

Evolving Systems, Inc. (NASDAQ: EVOL) is a real-time digital engagement accelerator. With 100+ customers across 5 continents, its solutions empower brands to increase revenue per user, reduce friction, improve retention, and maximize customer satisfaction. Our goal is to provide operators, brands and advertizers with the tools they need to manage their brand's interaction with consumers over mobile.

Evolving Systems, Inc. 9800 PYRAMID COURT, SUITE 400 Englewood, Colorado 80112 T: +1 303 802 1000

Regional Offices: France, Romania, India, Malaysia, U.K.

info@evolving.com

White Paper l Introducing Next Generation Loyalty © 2018 EVOLVING SYSTEMS All Rights Reserved 4

Contact our team of experts at Evolving Systems to find out how E4O can help you capture and convert mobile phone users into loyal and profitable customers. Call us now on +1 303 802 1000 or email us at .info@evolving.com

CASE STUDY Driving retention and revenue growth with behavioral insight

Challenges: An operator based in Europe faced:

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 behavior and gaining insight into how the customer interacts with the brand, it became clear that not all customers in a specific ARPU segment had the same needs. Customers with a clear preference for text were previously offered voice offers,

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

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

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