By Lia Grimberg, Principle and Consultant, Radicle Loyalty
Your customer data is a treasure trove of information. Your customers are shouting at you to use this data to understand them better, to value them as human beings, and to provide them with real value in exchange for their data. Collecting this data and not utilizing it could cost you! For example, according to Gartner, brands risk losing 38% of their customers because of poor marketing personalization efforts.
We collect reams of data! But too often we, as marketers, get inundated with the big question: what should we do with our data? Just first-party data consists of all transactions, web interaction, loyalty program behaviour, and calls to customer service, to name a few. To add, we also have zero-party data collected through surveys and even purchased third-party data. How do we make heads and tails of it all?
I would suggest starting with the end in mind.
1. Determine the goals and objectives of digging for insights.
What is your vision of success? What are you hoping to accomplish? Is there a question you want to answer?
You need to be clear on your expected outcome and the metrics you want to drive. Are you trying to improve conversions or increase retention? Do you want to drive incremental purchases or margin? Do you want to acquire more customers or to understand your existing customers better to design more effective products and services? Those are all great analytical avenues to pursue, and each one would take you down a different analytical path.
2. Start somewhere and don’t worry about getting it perfect.
Depending on the metric of focus you chose in the first step, the type of analysis you would need will become clear. For instance, if you are worried about retention, you would need an attrition model. If you want to drive acquisition, perhaps look-alike and likelihood-to-refer models may be useful.
If you are still stumped with where to start, don’t fret. You can look at your data from a business-first point of view, such as with a simple RFM (Recency, Frequency, Monetary) model trend to see which customers are disengaging or could be more engaged.
An RFM model and scoring mechanism is an easy first step to understanding and differentiating among customers by their value. It allows you to segment your customers into several tiers (let’s say 3-5 for the sake of simplicity) based on how much they spend with you, how recent was their last purchase, and how frequently they make purchases.
Especially important is to monitor the migration of customers among tiers over time. For instance, is the majority of members migrating up or down the tiers? I would be very concerned if a great majority of my best customers suddenly migrated to the next best tier and delighted if it were the reverse. It is equally important to understand the situation of those formerly best customers moving down from the top tier. What is causing that downward shift and how can you prevent it?
3. Action the data
Now that you have some insights into your customers, the worst thing you can do is sit on the data. Doing nothing with the insights doesn’t benefit your business or your customers. Don’t fall into the trap of analysis paralysis.
You have a model in place. Now develop a few hypotheses as to what caused it or survey relevant customers. Based on the outcome, outline what you believe can remedy the negative situation or amplify the positive situation. Then proceed with a test. If you had a few weeks and €100, what would you offer these customers and what would you say to them? Ensure that this communication and offer is reasonable given your expectation of their response and behaviour change in the short term.
Based on the results of the initial test, you can iterate and optimize every component of the communication, including the offer (most effective), channel, subject line, copy, and creative. Then you can create a turn-key program with some test elements to continue improvements.
Go back to your initial goal and track against it. You can start this process again with a new goal in mind.
Lia Grimberg is the Principal of Radicle Loyalty, a personalization and loyalty consulting firm. Radicle Loyalty helps you find the root of the issue, the radicle. We analyze your data, gather insights, and use it to personalize your communications with your customers. Radicle Loyalty creates marketing strategies and designs loyalty programs to correct customer behaviour and drive emotional loyalty.
With 20+ years in loyalty as a practitioner and a consultant, Lia honed her loyalty and marketing skills during her corporate career at companies such as The Bay, Loblaw, LoyaltyOne, The Home Depot, and American Express.
Lia holds an MBA from The Schulich School of Business at York University.
