The power of sophisticated technology is no longer the privilege of technologists alone. It has moved into the hands of the common man. This consumerization of technology has put the customer in the driver’s seat and retailers are expected to deliver more convenience, more consistency, more collaboration and more customization than ever before. This evolution is forcing the once predictable retail industry, to make the customer as the focus of every merchandise, marketing, store and supply chain decisions.
As the industry shifts focus from pushing profitable products and goods to catering to the digitally empowered customer, customer engagement is playing a crucial role in bringing in the moolah. Today’s retailers have many channels, locations and devices to converse with their customers. Be it building a platform that supports omnichannel communication, installing indoor positioning systems that communicate in-store, empowering store associates with insights for effective clienteling, retailers are leaving no stones unturned. But what’s the low hanging fruit that every retailer HAS to get right? Email newsletters. Email newsletters are a great way to continue the conversation post a purchase, pave the way for further sales and keep your brand on top of the customer’s mind. This continued engagement has been a staple for any business and the difference between a great newsletter and one that moves into spam is, drumrolls, ‘personalization’.
Email personalization has been around for a while now and there are some rudimentary tick marks that any basic CRM can offer you. Things like: adding the first name of the customer after a hello, scheduling emails to go out at times that the user has opened emails in the past, setting up a flowchart of ‘if this then that’ messages, etc. But to drive maximum outcomes out of newsletters one must look at the customer as an individual and fully understand who s/he is as a person before marketing to them. Agreed, doing this at scale might be a challenge, but that’s what segmentation is for. The more time you spend in segmenting and micro-segmenting your target audience, the better the results of your newsletters. Let me elaborate with these three examples/ tips that you can use to personalize your newsletters.
Understand that your customer data is dynamic and evolves over time
The database to send newsletters either comes through the loyalty card/ membership data the retailer already has or acquires a ‘fresh’ database from a service provider that charges anywhere from 5c to $10 for each email id. Majority of promotions are run on these email ids that the retailer has acquired over a period of time. If your team regularly cleanses this data, you are solving the issues of bounce rates, open rates etc. but the click rates? Click rates require you to offer up the right recommendation to the individual and the individual has to receive the most contextual offer that matches his lifestyle at that very point in time. For example, your loyalty data says that Mr. X bought $50 worth of meal kit on Jan 2016. This data has entered into your system and has been lying there for more than two years. Now your marketing team is looking to promote a newly launched, ‘fit and fab’ meal kit and Mr. X unwittingly enters into the newsletter list. But hey, he never clicked on the offer you sent him. Do we need to cleanse the DB again!!?
What really happened is that Mr. X got married to Ms. Y in Jan 2017 and started shopping for grocery items instead of meal kits. He also increased his share of wallet to $500 per month by shopping for ration supplies for the entire week. He drops into the store every Sunday evening to pick up stuff for the rest of the week. It’s all there in your loyalty data.
Now, as a retailer who wants to personalize newsletters, would you look at individual purchase items or look at buying patterns and understand the intuitive meaning that every basket has? Micro-segmentation of your database just solves one part of the puzzle, but to really personalize offerings, you need to devise strategies and have systems in place to allow movement of contacts within different segments upon every new purchase. Your system should be able to tell you that Mr. X did not respond to the meal kit offer you sent him, remove him from the DB, look at his shopping patterns and recommend that you send him a ‘20% off on Organic tomatoes’ coupon. If you have your marketing team aligned with this thought, then its bound to increase conversions. But if you have systems that have recommendation algorithms that can do this at scale, every single time, even better!
Look at ‘like to like behavior’ of similar individuals
If you are going to feed a stray dog, are you going to give him broccoli or left-over meat? The meat of course. Now how do you know that the stray would prefer meat as opposed to broccoli? Have you observed its eating habits over a period of time? Of course not. You have a dog at home that would give you a cold stare if you fed him anything other than meat. You have learned that if my dog would not prefer broccoli, then the stray would not either. It’s a logical conclusion that you arrived at by assuming that dogs, in general, prefer meat because my dog eats meat. Extrapolate this thought to your marketing. (Ah, the things your dog teaches you!).
You have 10 items in your new product catalog that you have to market in your newsletter. Let’s take 3 different segments. Segment 1 – bought dairy and eggs. Segment 2 – bought meat, wine, and beer. Segment 3 – bought diary, eggs and chocolate sauce. Now, if one the 10 items you were to market was chocolate sauce, which segment would you pick? Segment 3. But based on purchase data, the baskets of segment 1 and segment 3 is almost the same. Can we try offering up a discount on the chocolate sauce for segment 1 based on the sales volume that segment 3 is seeing? Let’s label segment 3 as ‘Bakers’ and segment 3 as ‘suspected bakers’. See what happened there? You have created a microsegment based on purchase patterns and created a cohort called as ‘suspected bakers’. People can move in and out of this mailing list depending upon whether they purchase the chocolate sauce or not. Once you deploy such strategies to your database, you are effectively moving away from ‘trial and error’ marketing which cannot hold a candle to personalized marketing.
Pop quiz – which segment would you categorize as ‘weekend barbeque’?
Many messages, same database
Yes, audience exhaustion is real. The more the customer opens your emails and finds out that there is nothing interesting in there, the more your response rates and ultimately sales, suffer. It is tempting to send an exciting new product that you are launching in the store, but don’t give in! The same customer is present in your ‘bakers’, ‘suspected bakers’ and your ‘weekend barbeque’ lists. Are you going to send the same, ‘new product discount on ketchup’ three times? Surely, touch monitoring systems will detect send frequencies and alert you about potential overlaps, but they just look at time stamps against a particular id and do not recommend what message you need to send them.
This is where your team needs to do the due diligence and spend considerable time effort in creating microsegments. And stick to it. If you have identified ‘weekend barbeque’ as a cohort, you must stick to messaging that is appealing to this cohort. This messaging can be around the lifestyle that a weekend barbeque enthusiast might lead. For example, you send him recipes for killer BBQ ribs in week 1, discount on BBQ sauces on week 3, invite for a store cookout competition on week 5. If the contact does not respond to any of these, then it’s time to move him out of the cohort. But imagine the loyalty that this sort of message is bound to bring (and the sales).
You could also try segmenting based on the specific shopping behavior like ‘most likely to respond to coupons’, ‘most likely to respond to try new products’, but until you get to segmenting customers as ‘most likely to respond to a coupon on organic eggs’, you are not acing personalization.