Do more with your data after busy shopping periods: five minutes with the data gurus

This is part one of a three-part series on where to focus your efforts after a surge in sales.

Keep your eyes peeled for part two.

Congratulations! Your business has weathered one of the busiest shopping periods of the year. But whether it’s Black Friday, Christmas, January,scorching summer sales, or even Halloween bonanzas, we all know that the work doesn’t stop post-event. It’s not just distribution and accounts that will have their hands full now – at least it shouldn’t be. As a marketer or a CX professional, this is where you can really get your hands dirty with data.

We speak with Ian Pollard, one of our senior product managers, and Sam Crawley, a product data scientist, to uncover what you should pay attention to once the dust has settled on a busy shopping period, and how to make the most of it in Engagement Cloud.

What are the most common challenges you hear about from retail customers following a surge in sales? Did this inform the product design of Engagement Cloud?

Ian: If you’ve just run a big sale, you’ll have some newly acquired customers. Having spent big on acquisition and likely taken a margin hit with discounting, you don’t want to lose them. Getting that all-important second purchase is the difference between never hearing from them again and building loyalty. This was what led us to build out the ‘single purchase customer’ tile as one of the nine metrics Engagement Cloud users can keep a close eye on and drill down into. 

Sam: There are nine tiles in total and things to learn from all of them, especially after a big sales event, as long as you keep context in mind! The ‘average items per order tile’, for example, might show that during the sale people were either picking up several discounted items or in fact buying lots at once. The latter might indicate a successful use of on-site product recommendations.

As Ian mentioned, the acquisition of new customers is a major part of these events, and many of them may not end up re-purchasing at all. It’s important to keep the average value of these newly acquired customers in mind, especially when comparing to the amount of money and effort that went in to acquiring them. Drilling down into the CLV tile, for instance, might give an idea of the ROI you’d expect compared with a standard period.

Now if you were head of marketing or customer experience, what would you do with this data? How would it help you achieve your goals of improving ROI, lifetime value, or overall customer experience?

Ian: Our segment builder lets you target customers by RFM persona. Drag in the RFM data block and target anyone in the ‘recent customers’ persona.

Marketing to these people is difficult at such an early stage of the relationship; all you really know is that they’re a new customer. They may have nothing else in common with each other. For this reason, I would follow up by using the most reliable data point you have — what they just bought. 

To target effectively against that, I recommend using our ‘also bought’ product recommendation. This looks at the highest value item in the recent checkout and finds other shoppers who have also purchased it. Within that group of shoppers, Engagement Cloud will then find other products they have bought and recommend the most popular. 

Sam: There is no magical method to improve ROI or lifetime value, but different marketing methods can be optimized and refined over time in order to see more success. This is where context becomes important.

We’ve given you the ability to filter the metrics and drill down reports on specific segments or RFM personas. What this means is actually really cool. You can trial different methods on different categories of customers. Then you can compare the effects on CLV and ‘average delay’ over time by selecting different date ranges.

Use these tools to find what works best for you and your customers.

Ian, what was the drive behind developing the recency, frequency, and monetary (RFM) personas (as well as the persona movement reports) in Engagement Cloud? What value do these data-driven metrics bring to a business?

Ian: RFM had been in our plans for a while and we knew it was a popular wish-list feature with customers. The ability to manually create RFM-like segments had always been possible in Engagement Cloud, so the decision to make a formal data model for it wasn’t something we rushed into.

 I’m really pleased with our model: it took a lot of thought, but I think it’s the right balance of power and simplicity. The core model is built around six very-easy-to-understand personas grouped across a lifecycle timeline familiar to any retailer — inactive, lapsing, active. It’s incredibly valuable to anyone wanting to do behavior-based targeting or reporting.

The movement reporting came from insights we uncovered whilst building the RFM model. Some of our customers were really interested in how customers moved through personas over time. That stuck with us and we started modelling these movements and found interesting stories in the data. Finding a way to show this to our customers was a little more of a challenge. We have some big opinions on data visualization in the team, but I think we’re all happy with where we ended up. Even if we did need to define a whole new color palette to make it work!

Which personas should businesses keep an eye on? And how should they be treated after a large sales event?

Sam: New customers, for sure.  After a large sales event you are likely to have a much larger chunk of new customers than normal, and this represents a great opportunity to increase your loyal customer base.  You should focus on marketing to these people, with the aim of converting them into repeat customers.  Make use of the persona movement report to keep track of them and figure out which tactics work best.

Any other advice on doing more with data for businesses using (or thinking of using…) Engagement Cloud?

Ian: We have a great feature called web behavior tracking (WBT). It tracks page views, and, when we can identify the contact, it matches those web sessions with them. If you combine WBT with RFM, you get the ability to identify emerging purchase intent. 

Why does that matter?

Ian: Think about a win-back campaign for your inactive customers. You’ve already paid to acquire them and they’re now giving you every sign that they can realistically be won back. They’re worth spending money on, they’re your best leads.

I would create a multi-stage and multi-channel campaign. If they don’t buy or engage via email, then re-target via Facebook, Instagram, or Google (which can you do via our program builder). If they engage again on your website but still don’t buy, then it may be worth looking at a coupon campaign. 

Any top tips Sam?

Sam: Try combining automation and programs with the persona movement report.  The report isn’t just useful for tracking what happens to your new customers after a sale, but can be used to see what the overall engagement lifecycle of your customers looks like. Filtering based on segments might reveal insights into what can be improved in your automation and programs, or where you are excelling.

Thanks both!

And don’t forget, this is the first of our three-part series in what to do after a surge in sales. Click here for part two, or sign up for blog updates and more here.

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