The Customer and Marketing Analytics Maturity Model

Transcription

1 EBOOK The Customer and Marketing Analytics Maturity Model JOE DALTON, SMARTFOCUS $

2 INTRODUCTION Introduction Customers are engaging with businesses across an increasing number of touch points websites, social media, instore, mobile and tablets. At each touch point, customers expect a customized and personalized experience optimized specifically for them. New levels of flexibility and convenience that improve their experience are great for the customer. But the increased customer expectation continues to be a challenge for businesses, which have to manipulate enormous amounts of data to try to understand how to effectively engage each individual. This challenge remains a burden for many businesses, but for those that have the right customer analytics plan this could rapidly become a competitive opportunity. In this ebook, you ll find out more about how your business can capitalize on analytics technology whether you re a new adopter or a seasoned expert. SMARTFOCUS EBOOK PAGE 2

3 WHERE SHOULD WE START? Where should we start? Companies large and small are wrestling with questions relating to BI (business intelligence) and analytics investments as never before. If you ve been around the BI market for a while you ll know that interest and spending on analytics applications waxes and wanes depending on; competitive pressures (what is Amazon doing?), the availability of new data sources (social data), technology advancements (Hadoop, etc.), and either overfunded or underfunded past BI projects. Going into 2015 all indications are that interest and investment appetites in analytics is at an all-time high and getting stronger. Whether you re a seasoned veteran or a newcomer to these types of applications, it s helpful to think about investments in this area in terms of a maturity curve. In other words, if we re new to analytics applications: where should we start? and if we ve been deploying these applications for some time: where should we go from here???? SMARTFOCUS EBOOK PAGE 3

4 AN ANALYTICS MATURITY MODEL An Analytics Maturity Model If you think about investments in analytics projects on a cost vs. business value basis (which includes software licensing, hardware or cloud computing expenses, professional services, user training, and internal integrations with either data or applications) we can get a sense of how projects compare with each other, how we might prioritize multiple projects over time, and frankly whether any given project is worth the effort. All of this might sound obvious, but there s been more than a few analytics/bi projects that have ended up upside down in expense vs. business value simply because this kind of analysis wasn t done straight away. Looking specifically at customer and marketing analytics, here is one way to look at a progression of investments that a) build on each other, and b) offer business value that justifies their expense. This can also be viewed as a customer personalization maturity model, since each step in the progression enables the organization to deliver a more coordinated, personalized experience for their customers, which of course is the ultimate objective. Let s discuss each of these separately. SMARTFOCUS EBOOK PAGE 4

5 ANALYTIC COMPLEXITY (COST) VS PERSONAZATION VALUE (BENEFIT) Analytic Complexity (Cost) Marketing Data Integration Audience Targeting (Segmentation) Marketing Attribution Next Best Offer / Segment / Channel Personalization Value (Benefit) SMARTFOCUS EBOOK PAGE 5

6 MARKETING DATA INVENTORY Marketing Data Inventory This is the prerequisite step that enables all of the follow-on projects or application investments. Whether you re looking at a SaaS deployment or an on-premises deployment, kicking off a project that takes inventory of your customer centric data is time well spent. Sometimes this step gets combined with another application step, such as customer segmentation, but it s recommended to look at this more holistically. First, create an inventory of all of your data sources and data producing applications that contribute to your customers identity, behavior, and activity profile. Typically this involves purchase history data, CRM data, marketing activity data, ecommerce data, any purchased or 3rd party data, social data, review data, etc. Once you have an inventory of all of the data you could pull together into one system, rank each source of data by the effort it would take to access it and either copy or index it into your marketing data store (depending on how you want to access it). Low, Medium, High rankings work well here, you don t need to spend a lot of time detailing the integration costs at this point. Make sure you include data from systems and projects that are currently planned or in-progress, such as in-store ibeacon pilots, customer loyalty projects, etc. You ll want to keep a six-month horizon view of what customer and marketing data is coming online. SMARTFOCUS EBOOK PAGE 6

7 MARKETING DATA INVENTORY This should result in a table that looks something like this: Data Source History Available Customer ID Key Source System Effort to Access Business Value activity 6 months address marketing High Medium Browsing History 6 months internal ID Google analytics Low Medium Customer Demo 2 years street address CRM High High In store browsing (ibeacon) Pilot address Loyalty app Low High Social Sentiment 1 year address Social listening platform Medium High Purchase history 3 years address Ecommerce platform Medium High SMARTFOCUS EBOOK PAGE 7

8 MARKETING DATA INVENTORY Include all of the attributes you can, and a sample row of data from each system if possible. Keeping your data inventory profile up-to-date once per quarter is a good idea systems change, new data sources become available, and so forth. Another way to think of this is to create a customer-centric diagram, outlining all of the possible touch points your customers interact with, and all of the data sources that your customers create through those interactions. $ Once your customer/marketing data inventory is complete you can move on to creating a data store (which could be virtual). Here, you can do your customer and marketing analytics. If you have the resources that can access a data store natively (i.e. data scientists), then kicking off a project to realize a customer/marketing data store makes sense. If you don t, then you ll need to move to the next application step to justify this expense. SMARTFOCUS EBOOK PAGE 8

9 AUDIENCE TARGETING (SEGMENTATION) Audience Targeting (Segmentation) Most companies perform some sort of customer segmentation, usually as part of one or more marketing applications currently in use. marketing systems are a good example of this. There is often a way to segment your universal customer list with a variety of filters before executing an campaign. However, when looked at from a stand-alone, analyticsdriven perspective these systems often fall short of what the company needs to improve its marketing effectiveness. How should we be segmenting our customer base? How many segments can we effectively market to? How can we track our marketing effectiveness by segment? How do new customers respond to our marketing efforts compared to our loyal customers? How can we leverage historical behavior or purchase data? These and questions like these are usually raised by marketers and analysts when planning for a more robust customer analytics and segmentation investment. This is usually the best place to make an incremental investment in customer analytics after you ve completed your data inventory step, for the simple reason that a good foundation in customer discovery and segmentation capabilities helps improve every downstream application investment. Once your team discovers and defines a list of your own customer segments or micro-segments and you have the ability to track their performance and migration, optimizing your marketing spend for customer segment performance becomes much easier. Whether you choose to tackle this step with an off-the-shelf application or an in-house application project, the end result should be a list of customer segment definitions that the whole company can agree on and that you will measure over time. Something that works very well here is a simple scorecard that measures certain key performance metrics per segment and sets a goal for improving those metrics over a defined timeframe. Here s an example: SMARTFOCUS EBOOK PAGE 9

10 AUDIENCE TARGETING (SEGMENTATION) Customer Current Target Current Average Target Average Current Target Current Target Segment Members Members Order Value Order Value Frequency Frequency Annual Spend Annual Spend Loyals $55 $ $19,866,000 $25,308,000 Brand New $25 $ $1,350,000 $2,041,200 Occasionals $18 $ $432,000 $720,000 Note that you don t need to have a very big percentage improvement in any of the metrics to see positive results. Usually a 1-2% improvement in any of the metrics will result in considerable bottom line movement. This is precisely the power of a good customer profiling and segmentation strategy done right, it can show you with clarity exactly what levers to pull, for which segment, to deliver the best results for the business. As an example, suppose we defined our loyal segment as people who have spent at least $500 in the previous 12 months, and who also have at least four transactions with us in the previous six months. Using a robust customer analytics application, we can then focus on improving the performance of this segment by improving either their frequency of transactions, their average transaction value, or both. Using affinity analysis (or market basket analysis) on this segment s transactions, for example, you can determine the right items to recommend for cross-sell or up-sell on a personalized level. At SmartFocus, we work we clients who have seen cross-sell and up-sell personalization raise average order values by over 50%, although even a 2% or 3% improvement in AOV for large segments delivers very large incremental revenue. Similarly, determining brand preferences for the people in your new customer segment, and using that to drive your marketing on-open personalization typically delivers substantial increases in open and click-through rates. SMARTFOCUS EBOOK PAGE 10

11 MARKETING ATTRIBUTION Marketing Attribution At this point, after investing in a holistic marketing data environment as well as a robust customer analytics and segmentation application, we are ready to tackle more analytically intense projects and applications. Having an understanding of what marketing channels work better than others for each of your customer marketing segments is a good next step. To do this, we need to refer back to our data inventory table to see what marketing activity data we can capture prior to each customer s transaction and what data we would like to have. In most cases, you will be able to access all of your marketing data (sends, opens, clicks, bounces, etc) by customer, as well as your customer s online marketing interactions (certain website pages, display ads, search term click-throughs, etc.). If your organization does offline marketing (for example, catalogs or direct mail), you ll want to take in that data as well. The primary objective of a good marketing attribution application is very straightforward: help us spend more on what works and less on what doesn t. There are many attribution methods in use today, but the concept behind all of them is the same: attribute each transaction to the marketing events that preceded that transaction according to certain rules. The most basic of these is either last click or first click, which gives credit for the entire transaction accordingly. In practice, first and last click models are not that useful. Equal Weight and Fractional models are quite useful, however, and are relatively easy to implement. These models attribute a portion of each transaction to many marketing actions that preceded it, giving a much more accurate picture of how your marketing spend may or may not have affected the transaction. A more powerful attribution model will also allow you to look at attribution by customer segment, so you can determine which marketing channels work well for each segment, and which don t. Here is an example of an attribution analysis by segment. SMARTFOCUS EBOOK PAGE 11

12 MARKETING ATTRIBUTION Precent of Total Dollars by Marketing Channel - From 12/01/2013 to 12/31/ % 40% 20% 0% Display Paid Search Organic In your own company, you might discover that certain marketing channels, like , work well for older demographic segments but not so well for younger customers. Marketing attribution can help you dial in the right marketing mix for each of your segments. SMARTFOCUS EBOOK PAGE 12

13 PREDICTIVE MODELING (NEXT BEST OFFER) Predictive Modeling (Next Best Offer) Its true that most analytic projects and applications are very good at looking at the past, which is important when trying to measure your customers behavior or your marketing effectiveness. Predictive modeling, however, is the practice of using past measurements to make a projection about what is likely to happen in the future. Most often, predictive analytics projects attempt to score customers according to how likely they are to take some action make a purchase, respond to a marketing campaign, etc. Not so long ago, predictive modeling projects were almost exclusively done as expensive custom projects by in-house data scientists and developers there simply weren t very many options for commercial applications that were both usable by the line-of-business professionals and provided the lift required to justify the expense. Today that is no longer the case. The availability of rich data sources as well as a number of options for off-the-shelf applications has significantly lowered the cost of deploying these systems, and thus raised their ROI considerably. Building on the previous steps of data inventory, customer analytics, and marketing attribution, all of these can be used to fuel comprehensive and effective predictive modeling applications that can greatly improve the effectiveness of your marketing spend. One of the most effective initial use cases to tackle in this area is propensity to purchase. For a multi-department retailer, for example, giving a marketer the power to select customers who are likely to purchase from a certain department within a certain timeframe is very helpful in optimizing marketing spend. Savvy marketers know not to spend precious marketing budget on two groups of people: those who are unlikely to purchase under any circumstances, and those who are highly likely to purchase anyway. Deploying a propensity to purchase application that allows the marketer to select a group of people who have a purchase propensity score of between 50% an 80% results in much more targeted and effective marketing budget allocations. SMARTFOCUS EBOOK PAGE 13

14 SUMMARY Summary Today s consumers are demanding coordinated, relevant marketing interactions in order to remain loyal. Customer and marketing analytics that drive improved personalization techniques are must-have investments for business to consumer organizations wishing to remain competitive. Knowing where your organization is on the customer analytics maturity model can help you assess and prioritize your next investments in this area and deliver the best results. Keeping a dynamic inventory of your customer and marketing data, having the ability to explore, define, and track customer segments and having the ability to optimize your marketing channel mix, as well as knowing which of your customers are likely to respond are all defined steps you can improve your marketing effectiveness. SMARTFOCUS EBOOK PAGE 14

15 The Customer and Marketing Analytics Maturity Model Joe Dalton, SmartFocus SmartFocus US Inc Suite #300, SE 30th Place, Bellevue, WA 98007, USA Tel: +1 (425)