Finding New Customers in Your Best Customers
Through a special arrangement, presented here for discussion is a series of recent articles from Lenati’s blog.
In practice, customer acquisition strategies involving segmentation often draw artful conclusions that lack real-world application. Often there is disparate implementation across organizations. When acted upon, they fail to deliver intended results. In these situations, companies can benefit by taking a different approach to customer segmentation and targeting by using "bright spot" analysis.
This approach can help:
- Drive higher ROI and customer lifetime value (CLV) by identifying segmentation characteristics and attributes that improve targeting.
- Improve positioning and messaging by uncovering unexpected brand or product attributes that resonate with customers.
The bright spot approach focuses on mining current customer data for pockets of success. These customers may not align with the decided acquisition segmentation strategy, but ultimately they represent the most loyal and valuable customers. A company’s goal should be to not only identify bright spot customers, but understand why they are so loyal. As Chip and Dan Heath, authors of Switch and Made to Stick, state, companies should ask, "What’s working and how can [we] do more of it?"
The bright spot approach to mining your customer base can be accomplished with different analytical methods. One approach, RFM segmentation, groups customers into clusters based on the Recency, Frequency, and Monetary value of purchases. Once isolated, the characteristics of the bright spot customers are assessed to determine their common demographic and behavioral attributes. These attributes are leveraged to identify actionable profiles of the acquisition targets.
A national online retailer applied this strategy when launching a new category. The retailer used RFM analysis to create six clusters and isolated the top 12 percent of customers as their bright spots. Behavioral profiling of this group identified that these customers had been shopping with this online retailer for many years, had purchased in categories similar to the new category and were members of a loyalty program. These attributes were used to identify which prospects to target for acquisition.
This bright spot approach was then tested in a controlled e-mail environment. The same promotional e-mail was sent to both the "treatment group," prospects that have similar attributes to the bright spots customers, and the "control group," prospects identified by the retailers’ legacy targeting method. The results of this test showed the treatment group purchasing 35 percent more new category products than the control group. If the bright spot theory is correct, this group of customers is also more likely to make purchases in the future.
The bright spot analysis takes acquisition strategies to the next level by helping businesses target and acquire new high value customers, based on what they already know.
Discussion Questions
What do you think of the “Bright Spots” approach to customer acquisition strategies described in the article and the overall merits of retailers targeting customers with similar profiles to their best customers?
Over the years this type of analysis took on the moniker ‘look-alikes” in the supermarket channel. That is, finding other shoppers that share the same variant characteristics as those that are currently the retailer’s best shoppers.
These strategies have had mixed success in my experience. When effective, the communication to these look-alike households has been impactful, compelling and relevant and most importantly, repetitive. Many times, however, look-alike shoppers often have other compelling reasons for not becoming a customer. Foremost among these are convenience and familiarity to another similar retailer. In addition, if that other retailer is doing a reasonable job in satisfying the shopper’s needs, they have earned “first right of refusal” and these shoppers will likely not defect to the acquiring retailer.
Further and finally, many times, bright spot analysis relies on syndicated data, matching household attributes of those that are your best shoppers with new households that should be, based upon these attribute matches. Although syndicated data is becoming a tighter fit as tech improves, there is still a significant margin of error factor whenever using syndicated and third-party data sources.
All in all, still a worthwhile strategy, but one that should be tempered with the appropriate caveats and limitations.
Getting the shopper to make the next buy is even tougher then getting them to make their first buy or purchase. This is a well known retail challenge, and leveraging technology to get the next buy faster is the trick to gaining more market share. Targeting via profiles, etc., are all great, but retailers need to watch the backside too – the supply chain. You can find a great bright spot and see it go dark fast if the product the shopper wants is not available RIGHT when they want it. Bright spot programs are about NOW, and having the inventory available NOW is vital.
Be Bright!
Many successful approaches are often common sense but rarely common knowledge. The logic and execution of the outlined RFM approach is brilliant. Retailers and brands do not focus enough on identifying and concentrating on their existing customers. The RFM “bright spot” process draws attention and forces an understanding of an existing customer. The information that can lead to more purchasing opportunities exists but is subtle and therefore often masked within the ‘noise’ of traditional data analysis.
A member of our team has been exploring and implementing new data analysis methods wherein significant insights can be exposed within data that is overlooked by traditional methods. The RFM “bright spots” approach is another exciting method that focuses on your existing top customers rather than just trying to drag the ocean for ‘new’ customers that may purchase once or twice. Identifying and focusing on existing customers and turning those into ‘customers for life’ should be the goal of today’s innovative marketers.
Mark makes some very relevant points and I would agree. There may be compelling reasons to not switch to a retailer’s store, such as service, location or familiarity with another store that simply keep shoppers of that store loyal to them. And the point on syndicated data can’t be stressed enough. Real data based on actual consumer insights is one thing, but syndicated/third party data is directional but oftentimes may not be accurate enough.
Giving traditional marketing research analysis a new name does not change the fact that gaining a better understanding of customer segments can be useful.
Great comments so far! I would also say that how you target these people is more important than actually finding who to target. There are plenty of analytics tools available today (as there have been for some years now) that can perform this identification task effectively. The larger challenge is to determine the appropriate actions arising from the new insights.
Each retail AND CPG brand has to know their brand assets and the intrinsic value of them to their best customers. Once those valuable assets that drive true loyalty are identified, then leveraging the promotional vehicles that highlight those assets will help drive profitable growth with new and existing customers.
It is always great to see a data-driven approach to customer acquisition recommended. Essentially, the “bright spots” approach focuses on identifying your best customers and then determining what is it about those customers that makes them different from the rest of the customer base. The factors that differentiate those customers are usually behavior-focused; behaviors consistently predict future behaviors better than demographics or any other factors.
By definition, the one piece you are missing in acquisition is customer behaviors with your brand or store. To replace those factors, the most successful approaches have been to append third-party data to your customer database, determine best customer behaviors in other categories, and then find prospects who have those behaviors in the other categories in the first place.
Test-and-control approaches can help you determine the incremental lift from such a strategy, but we have found it to be consistently higher predicting for e-commerce and retail clients.
Identifying “bright spots” may be good for teasing out the highest CLV customers. However, in today’s world there is another customer-based revenue stream: CRV, Customer Referral Value. The CRV is the revenue stream that comes from the customer’s referral sales, minus those buyers who would have bought anyway.
Interestingly, the heaviest buyers aren’t always the heaviest referrers. I’ve seen more than one study that shows the heaviest CRV contributors are medium to light buyers. The implications are interesting. Since these two groups of customers are different, and their behaviors are different, they should be incented accordingly.
Segmentation can be an excellent approach to help develop customer acquisition strategies. They provide a means to identify consumer/shopper targets whose attitudes and behaviors are consistent with a company’s goals, and, more specifically, what messages and product news would resonate with those targets. Segmentation also helps to determine the size of the opportunity – how many consumers there are in the target groups, but also how much sales volume they represent.
I think Bright Spots is a great marketing idea to help package and pitch RFM analysis with some new twist. I didn’t really see the twist in the piece. The insights were very high level – there were no nuggets – but to be fair, you can’t get clients to share the real insights.
I didn’t see the twist.
Hmmm…Just wondering if as much time and effort was spent on treating EVERY customer as your best customer, if the results would be even greater.
Then again, I’m not selling consulting services and analytic tools.
This approach assumes you have clean and accurate data about like households to your Bright Spot group. This alone should cause some concern. Garbage in, garbage out.
The idea of focusing on current customers is a good one. As past studies have shown, it is less expensive to keep and grow a current customer then attract a new one. Kroger, for example, has done very well using this model.
From our experience, there is no doubt that legacy “targeting” methods can be improved on with segmentation and other customer analytical techniques. Equally important, these approaches are just as valid, if not more so, for identifying the best existing customers to cross- or up-sell to.
Very interesting discussions – I’m enjoying all the feedback and look forward to continuing the conversation.
One point brought up by many of you is that your insights are only as good as your data. “Garbage in, garbage out.” I couldn’t agree more. Luckily, in online retail environments we often find an abundance of great data that is entirely driven by the customers interactions with the brand (website). Browsing, adding to cart, purchasing in multiple categories, billing zip code, billing name (for gender identification), loyalty program sign ups, etc… The key we can agree on is making all this data actionable and meaningful, with RFM analysis being just one of many methods.
I wanted to clarify that my test outlined here was based on actual customer insights and data, and not market research. I agree with many of you that decisions should be made on customer behavior and not only on 3rd party, industry analysis data.
One other idea that I would like your thoughts on is using RFM for both acquisition and customer retention. The concept outlined here was a cross-sell scenario. You have many categories in your business, each with their own P&L, and you want to acquire new customers in a particular category. In that scenario it makes sense to identify your best customers in that new category and look for more of them within your entire cross-category customer base.
This concept could be used for pure acquisition to your brand as well. You could identify the attributes of your “bright spots” and match this information up with market research to identify your acquisition targets outside of your current customer base. As noted above and in your comments, market research data is not always the most accurate.
What other analysis methods have you tried and seen success with?
With respect to the authors, the Bright Spot approach described here used in the RFM example has been used for years and a catchy proprietary name has been applied to bring the approach to life.
RFM analysis has traditionally been performed with the goal of identifying segments that have one key characteristic regarding value, growth potential, and risk of attrition.
Specific offers and campaigns are tested to meet the needs of the group and control group testing is a common method to evaluate the effectiveness of the approach.