Analytics for cross-channel campaigns

Analytics for cross-channel campaigns

Contents Introduction Introduction 3 Personalizing the interactions 4 What are the different types of analytics? 5 Measurement and insight 5 Segmentation 6 analytics 8 What analytics do I need for each level of sophistication? 10 Analytics level 1: One-to-all 10 Analytics level 2: One-to-many 12 Analytics level 3: One-to-few 14 Analytics level 4: One-to-one 16 Conclusion 18 There s no denying that marketing today is a two-way street. It s no longer enough to simply broadcast to your customers. Faithful followers of your brand demand conversation and acknowledgement of loyalty, and they demand it through the channels they dictate, not the other way round. The most successful campaigns build interactive dialog with the customer across all channels, optimizing contact throughout their journey. But if brands are to capitalize on this new breed of customer behavior, they must first put in place the analytics that will deliver the deep customer insights that are the hallmarks of a successful cross-channel campaign strategy. However, while it goes without saying that insight,, measurement and product recommendation s hould be components of any advanced cross-channel marketing platform, getting the right analytics varies according to the sophistication of your campaign, how frequently you interact with the customer and through which channels you interact with them. In this booklet, we ll take a look at the different types of analytics available, along with the benefits of each, and the essential customer data you need in order to build them. If you re looking to understand more about how and why analytics can help you increase your campaign s performance, then you ve come to the right place. 2 3

Personalizing the interactions Creating a truly optimized cross-channel marketing program takes a fair amount of work, and no campaign can jump from a channel execution state to a fully optimized cross-channel state overnight. But the type of analytics you use to reach a fully optimized state depends on how you interact with your customer. As you can see in the diagram below, these interactions can typically be represented as different degrees of personalization: One-to-All; One-to-Many; One-to-Few and One-to-One. What are the different types of analytics? The word analytics covers a broad spectrum of categories. Over the next few pages, we will be covering the different types of data analytics you will need for a successful cross-channel marketing campaign. Degree of personalization Measurement and insight One-to-One One-to-Few Insight Response attribution Insight insight Segment Cross-channel Segment value and churn scoring Product recommendation At the end of a campaign, every marketeer will ask the question: how has the campaign performed, and what has been the ROI from my marketing budget? Answering them will help you understand what s working well and what isn t, in order to replicate successes and improve areas where the campaign has underperformed. Sounds easy? In fact it s anything but. As you will see below, answering these questions can very quickly become a complicated process. First you need to differentiate the analytics into categories: One-to-Many Insight dashboard Segment Engagement mosaic Test and learn Campaign dashboard: This uses Key Performance Indicators (KPIs) to assess each campaign (from clicks to revenue generated), then benchmark its monthly performance against the previous month, against a similar campaign, or against market trends. One-to-All Insight Campaign reporting Segment Basic criteria dashboard: This helps you understand the campaign s effect on your customer base (whether the customer is new or inactive) or on particular segments. insight: To analyze which segments respond to which campaign or campaign type. Response attribution: To measure the effect of each component within the campaign (e.g. emails, text messages, banner advertising or social messaging) on the campaign s overall performance, then optimize your marketing budget between each channel. 4 5

Segmentation Other questions a marketeer needs to ask are: Who are my customers and what are their needs? What are their key differences? To answer these, you must regroup your customers into different segments, measure the audience and define a campaign plan that optimizes each segment. individual segment is better than another. It all depends on what you want to differentiate by. This is often linked to the level of personalization as you will see below. Basic one dimensional : This involves segmenting your portfolio by one particular factor that will be used across different campaign strategies. This could be anything from when the relationship started (new customers vs. old), age (junior vs. senior), activity (active vs. inactive) to recruitment sources (games vs. website). Engagement: To measure engagement, you segment your database according to how deeply the customer has engaged with your campaigns. You can measure this by recent and frequent clicks, open rates, or number of visits after push campaigns. value: This is when you segment your database according to each customer s value. You can measure this by revenue generated, or by recent purchases (or purchase frequency). Things can, however, become complex when you want to measure both online and offline revenue together e.g. mixing web-shop data and in-store loyalty card data. Churn: You segment your database according to the risk of customer churn. This means no longer measuring their levels of activity, like visits, clicks, or open rate, but instead, charting how their interaction has evolved compared with how they ve interacted with your campaign in the past. demographics and behavior: This means segmenting the database according to customers socio-demographics and behavior. You can easily achieve this by enriching your database using Experian s Mosaic. This helps you measure each customer s primary needs and therefore, their market potential. Channel preference: This involves segmenting your database according to your customers preferred engagement route or channel. Simply analyze all your cross-channel data, then choose the best channel every time you want to enter into a dialog with the customer. Micro : This is when you already segment your database into multiple axes, but want a single view that summarizes each one. Then you can gradually reduce the contact you have with each segment by segment and enrich each segment with customer surveys and feedback to understand the market trends and customer needs. eactivity segments click rates Advocastes Rocketers Faders Risers Bored Sleepers Occasional & Curious 50% 40% 30% 20% 10% 0% 2012-May 2012-Jun 2012-Jul 2012-Aug 2012-Sept 2012-Oct A1 Advocates A2 Rocketers A3 Risers A4 Faders A5 Bored A6 Sleepers A7 Occasional & Curious 6 7

analytics Leveraging predictive analytics means asking yourself: Who should I target with this product, or conversely, what is the best product for this type of customer? This helps you to target your campaigns more effectively, but more importantly, to adapt the message for different types of customer and therefore optimize your campaign s performance. Product recommendations: Using your product catalog, you can measure product similarity (products purchased by the same type of consumers) or cross-sell opportunities (products purchased in the same basket). You can do this using a recommendation engine that analyzes baskets or visits. This gives you the opportunity to personalize a message with you might also be interested in... or people who bought this product also bought. scoring: Using all the data you have available (product catalog, transactions, clicks and visits) you can assign each customer a score, according to their likelihood of buying a product. You can then target the customers that are most likely to make a purchase (or the best product to target them with) and engage them in a One-to-One conversation. This predictive scoring uses advanced techniques like machine learning capacities and can be calculated in real time, or by batch. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 45% 12% 91-100 100% 97% 98% 98% 92% 96% 100% 87% 93% 78% 86% 65% 77% 68% 58% 47% 36% 25% 81-90 71-80 61-70 51-60 41-50 31-40 21-30 11-20 0-10 Predicitive score % cumulative messages sent % cumulative clicks 8 9

What analytics do I need for each level of personalization? Level of analytics, needs and responses LEVEL 1 One-to-All LEVEL 2 One-to-Many LEVEL 3 One-to-Few LEVEL 4 One-to-One As you ll have discovered, the analytics you use depends on how sophisticated your brand interaction is. Let s take a look at what those levels of interaction look like over the next few pages. Analytics level 1: How to optimize the performance of One-to-All campaigns Measurement and insight Campaign dashboard Attribution model Overview: Companies marketing at this level usually focus on one particular channel (for instance, email) and create a marketing calendar that follows seasonal trends. Segmentation Objective: The main objective is to achieve a high-performing campaign, and you will usually measure this using KPIs like clicks, open rates and bounce rates to quantify effectiveness. Analytics responses: This involves consulting with your analytics provider on best practice then deciding which analytics you need. These include on campaign performance that will regularly measure specific KPIs and tell you when it s time to step up to the next level of personalization. You then use basic one dimensional to drive your campaign strategy without needing specific predictive analytics. analytics One dimensional Engagement value, churn and behaviour Product recommendation Channel preferences and micro scoring and price sensibilty Consultation Best practice Performance analysis and benchmarking insight Optimization program 10 11

What analytics do I need for each level of personalization?(continued) Level of analytics, needs and responses LEVEL 1 One-to-All LEVEL 2 One-to-Many LEVEL 3 One-to-Few LEVEL 4 One-to-One Analytics level 2: How to optimize the performance of One-to-Many campaigns Overview: Brands at this stage of marketing maturity are starting to optimize their activity within a particular channel. We often see campaign automation at this stage, and companies typically have a solid process for list selection and offer management. They may be using a number of different channels, but there is rarely interaction between them. Objective: The customer is central to the campaign, and segmenting for planning and monitoring is crucial to its success. You don t just want to measure the campaign s performance, you also need to measure the impact on your customer s lifecycle. Analytics responses: You use engagement to drive your strategy, along with a customer dashboard. This lets you measure the campaign s performance by consulting with your analytics provider on benchmarking and performance analysis. Your need for predictive analytics is still in its infancy. Measurement and insight Segmentation analytics Campaign dashboard One dimensional Engagement value, churn and behaviour Attribution model Channel preferences and micro Product recommendation scoring and price sensibilty Consultation Best practice Performance analysis and benchmarking insight Optimization program 12 13

What analytics do I need for each level of personalization?(continued) Level of analytics, needs and responses LEVEL 1 One-to-All LEVEL 2 One-to-Many LEVEL 3 One-to-Few LEVEL 4 One-to-One Analytics level 3: How to optimize the performance of One-to-Few campaigns Overview: At this stage, companies are starting to move to a single, unified database for all customer contact. Typically, organizations are working to develop cross-sell and upsell strategies and differentiate each strategy through segment and cross-channel execution. The database is now integrated with multiple customer engagement platforms and each channel has a clear voice for the customer. Measurement and insight Segmentation Campaign dashboard Attribution model Objective: The customer is at the center of the program within multiple different channels. Segmentation is needed to produce predictive analytics and in turn, optimize the targeting and relevancy of the message. Adapting your message according to segment becomes more and more important. One dimensional Engagement value, churn and behaviour Channel preferences and micro Analytics responses: You need to use multiple s like customer value, churn and customer behavior to drive your campaign strategy, and then adapt how frequently you contact them. Cross-referencing customer value and customer behavior highlights the customers in your database with high potential, but poor value. Conversely, cross-referencing high value and churn highlights customers at high risk of impacting on your campaign s performance. analytics Product recommendation scoring and price sensibilty You continue to monitor performance via a customer dashboard, including A/B testing and reporting. You develop an efficient contact strategy according to segment, and use a product recommendation engine and predictive analytics to adapt the message per segment. At this stage, a full scan of your portfolio using a customer insight consultancy program will help you measure how far away you are from the next level of personalization, as well as the potential benefits and costs. Consultation Best practice Performance analysis and benchmarking insight Optimization program 14 15

What analytics do I need for each level of personalization?(continued) Level of analytics, needs and responses LEVEL 1 One-to-All LEVEL 2 One-to-Many LEVEL 3 One-to-Few LEVEL 4 One-to-One Analytics level 4: How to optimize the performance of One-to-One campaigns Overview: In this final stage, you ve achieved a balance between your push and pull campaigns. You now have a continuous dialog with the customer that moves seamlessly between channels. Your primary focus is optimizing your interaction through cross-channel execution. Measurement and insight Campaign dashboard Attribution model Objective: You need to correlate your offers across every channel and personalize your customer messages based on their profile, how they re interacting and their recent behavior. Segmentation Analytics responses: You segment according to channel preference. You have rich data on when each customer reacts to a specific channel, right down to the day/hour. You now use micro, regrouping all axes and defining your strategy segment by segment, adapting them accordingly. One dimensional Engagement value, churn and behaviour Channel preferences and micro Then you add customers according to product preference to improve message personalization. You also could trigger a spot campaign based on a particular event and then target customers with the best product for them. You can also increase your penetration and improve profitability by targeting them with specific discounts or price points. To measure the results effectively, you need to take into account all channels. You will also need advanced response-attribution models to understand the how each action impacts your final result. analytics Product recommendation scoring and price sensibilty Consultation Best practice Performance analysis and benchmarking insight Optimization program 16 17

Conclusion The days of blanket marketing are long gone. Today s consumers are fickle, tech savvy and aware of being targeted. But we are also in a new era where customers are more open to marketing efforts, as long as they are relevant. s are already engaging across multiple channels. It s just a case of finding them, analyzing their behavior and then enriching your campaigns with the resulting data to give your consumers a better experience. As we said before, creating a truly optimized cross-channel marketing program takes work, and it doesn t happen instantly. But with the right analytics and insights, you can achieve better brand engagement, happier, more loyal customers, higher performing products and bigger profits. 18

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