Recency: Web Retailing Example, Part 2

The owner had learned a lot in the past few months, from understanding how to define an “active” customer to predicting the likelihood of a customer to respond to a promotion; from improving the profitability of newsletter promotions to actually being able to predict the profitability in advance based on the Recency buckets.  Despite all this, the owner of still had not answered the original question that started it all: why was the response rate to newsletter promotions falling while sales remain flat month to month?

The owner started packing boxes, thinking about how Recency had proved to be so significant a factor in customer behavior.  While packing, the owned recognized the names of best customers, and also the names of some of the new buyers.  Why were these new buyer names familiar?  

While pondering this question, the owner realized where the names had appeared before – on the daily new subscriber list to the newsletter.  Then an idea struck the owner like a bolt of lighting: since Recency of purchase predicted the likelihood to purchase again, is it possible that Recency of subscription could predict the likelihood to make the first purchase?  Again with this Recency thing?

The owner’s head was now spinning, this was too much, too fast.  It was a struggle just to go into the subscription records.  Figuring out when Newsletter Responders had joined the newsletter seemed like an insurmountable obstacle.  The owner was sweating, looking out at the spreadsheet with barely one eye open, murmuring “too much, too fast.”  The owner’s hand trembled on the keyboard, as the last table join came into view.

And there it was.

Looking at Only Buyers, they subscribed to the Newsletter How Many Days Before they made a First Purchase?

Days before First PurchasePercentage of Buyers Subscribing

The owner gasped aloud.  How long ago someone subscribed to the newsletter predicted whether they would respond to the newsletter with a purchase!  And how long ago someone responded to the newsletter and
made a purchase predicted if they would respond to the newsletter and make a purchase again!  It was like a chain of events, a sequence, with the likelihood of a customer to move to the next step in the sequence at any point in time ruled by the Recency of the customer for the prior step.

It’s that whole “cycle” thing showing up again – customers seem to pass through “stages” and these stages are predictable based on the previous behavior of the customer – as long as you know how to measure and track the behavior.  If you are using the wrong metrics, the owner thinks, you simply can’t see or take advantage of these cycles. 

Well, fellow Drillers, we know what the owner of is talking about, don’t we?  It’s the Customer LifeCycle, the most powerful tool you could possibly have to increase the ROI of a marketing campaign and increase the profitability of a customer.  If you understand what a customer is likely to do before they do it, then you can plan for and customize offerings and campaigns appropriately.

Marketing based on the Customer LifeCycle is “event-driven” marketing.  You use LifeCycle metrics like Recency to mark or define customer events, and then spend only when you have to, and always at the point of maximum impact.  This creates extremely high ROI marketing and service programs, because the targeting mirrors customer behavior. 

Most of the buying activity was coming from people who Recently joined the newsletter.  Since the number of new people per month joining the newsletter was flat, sales are flat month to month.  But as the newsletter list keeps growing each month, this “new blood” – new subscribers who are likely to respond and buy – becomes a smaller and smaller percentage of the total newsletter list.  So sales remain flat as response rate is falling.

Newsletter response riddle solved.

But how can you increase profits knowing this?  Well, if you offer discounts to people who have just signed up for the newsletter – the people already most likely to purchase – then you probably are giving away margin you don’t have to give away.  Save your discount budget for those less likely to purchase, customers who are deeper into the LifeCycle, the ones who are “less Recent.” 

The owner continues to ponder these cycles, with the likelihood of a customer to move to the next step in the sequence at any point in  time ruled by the Recency of the customer for the prior step.  The owner had read some stuff about CRM and wondered if this “sequence” was what all that CRM noise was about.  Maybe the owner should see if the rep who called yesterday “stands in awe” of  Perhaps, the owner thinks, I could learn “how to keep customers front and center” without hearing “Five Hundred It Is” from this rep. 

No, “CRM” would have to wait.  It now appears increasing new subscribers to the newsletter is the most important thing that can be done to increase sales, because Recent newsletter subscribers are the most likely to buy.  And after all these discoveries about Recency, the owner wondered if Recency could have something to offer on the newsletter subscription challenge.  What do you think?

The owner always tried to follow hot topics or trends talked about in the online community at the site, both in the newsletter and merchandising of the site.  The owner felt it was more logical to “put products in front of the traffic” than try to force or bend the traffic to come to the products.  Find a group of people and give them what they are already looking for – it’s same reason why   search marketing works so well for the site.  The owner was not sure if this was the right thing to do on the content side, but there was really no way to prove anything in this area.

For example, some parts of the site get very high traffic, others lower traffic.  Would more newsletter subs be generated if the high traffic areas had more content?  If the low traffic areas were downplayed in the navigation?  Who knows, and implementing would be a lot of work, so just guessing was not a good idea.

The owner had done some content analysis along these lines. There were 10 major content areas on the site; all the pages in a single area are set up as “Content Groups” in the visitor analysis tool the owner is using. This means the owner can track stats at a
macro level for content areas as a whole very easily, instead of having to deal with tracking down individual page views and aggregating them into a report.

The owner used these Content Group reports to detect “hot spots” on the site. By looking at the trend in total visits and page views over time, the owner got a good idea of where the interests of the audience were flowing, and used this information to “stand in front of” this topical traffic with articles and products. But was following the traffic really the right idea? Certainly not all traffic is created equal; quantity is not equal to quality. But what else would you look at?

Wouldn’t you know it, the owner had just upgraded the visitor analysis tool, and found out that it now supports visitor Recency as a native reporting metric right out of the box.

Just in the nick of time for this Example, eh?

So, the owner excitedly turns Visitor History on in the analysis tool, and it starts building a record of last visit date for each visitor. Then, looking at the 10 different Content Groups, the owner ran a report on the average Recency (average days since last visit) of visitors to each of the Content Groups. What do you think the owner found?  Yup.  Dramatic differences in average Recency by Content Group.  In fact, generally the Content Groups with high overall traffic had the worst average Recency (longest time since last visit), and the low traffic groups had the best average Recency (shortest time since last visit):

Content Groups, by Traffic and Recency

Traffic RankAverage Recency
136 days
232 days
324 days
429 days
527 days
623 days
714 days
817 days
9 7 days
10 4 days

Well, there’s a shocker, thinks the owner, who was getting used to this kind of slap upside the head from the Recency metric by now.  But it made sense.  The areas with specific, targeted content had the lowest traffic but this traffic was on average more Recent – visitors to these areas didn’t just “repeat,” they were Recent Repeaters.  The high traffic areas had relatively untargeted content so they drew a lot of activity but not a lot of loyalty; after a few visits that was it.

“How interesting” thinks the owner of  Perhaps the high traffic / low loyalty areas are frequented mostly by new visitors and potential customers, where the low traffic / high loyalty areas are frequented primarily by current customers.  Clearly there was an actionable idea in this chart, though it would take some more crunching with the visitor analysis tool to draw it out.

“Wait a minute,” the owner thinks.  “I have been tracking newsletter subscriptions by Content Group.  You don’t suppose…”

Well, fellow Driller, can you guess what the owner of is on to here?  What will the data say?  Here it is:

Content Groups, by Traffic, Recency, and Newsletter Conversion rate

Traffic RankAverage RecencyConversion: Subscribers
to Visits
136 days.2%
232 days.6%
324 days1.1%
429 days.9%
527 days1.4%
623 days1.7%
714 days3.8%
817 days2.1%
9 7 days5.6%
10 4 days7.2%

You guessed it.  The more Recent the Content Group, the higher the conversion of visits into newsletter subscriptions.  The owner, once again slack-jawed by the power of the Recency metric, sums it all up:

“On average, the more Recent the visitor is, the more likely they are to subscribe to the newsletter, relative to other visitors.

The more Recent the newsletter subscriber is, the more likely they are to make a purchase, relative to other newsletter subscribers.

The more Recent the last purchase is, the more likely the buyer is to make another purchase, relative to other buyers.”

The owner continues on, a bit breathless with the rush of all this stuff coming together.

“What I am seeing is that becoming a “best customer” on can be seen as a process, one that starts with a visit, moves on to a subscription, then a purchase, and hopefully multiple purchases.  I always sort of knew that; now I see it in action.  But what’s really powerful is I can rank each member of a group – visitors, subscribers, buyers – against all the other members of their group for likelihood to move forward in the process using the Recency metric.

Knowing this provides me with three benefits:

1.  Having the source of the visitor, subscriber, or buyer, I can “track backwards” and find out what sources (media type / offers) generate visitors most likely to become subscribers, buyers, and multi-buyers – and using Recency, predict which of those are most likely to complete any step in the LifeCycle process.

2.  I can customize communications to the members of each group based on their likelihood to move forward in this LifeCycle using Recency.  By addressing specific people with the right message at the right time (like I did with the discount ladder), I will generally get higher response and conversion of the visitor to multi-buyer while lowering my marketing and discount costs.

3.  I can track retention and failure to progress in the LifeCycle with Recency, and be proactive about trying to “save” customers who are in the process of defecting.  At any point in the visitor – subscriber – buyer – multi-buyer LifeCycle, I can track decreasing likelihood to progress and take special action with those who have high potential or current value based on their source or past buying behavior.”

It was late, and the owner was exhausted.  No point in trying to map out all the implications of these discoveries now, the owner thinks; this Recency thing was obviously quite powerful and it would take some time and testing a few ideas to fully develop.  

For example, rather than determining “what’s hot” just by visit volume, if I look at the Recency of visitors I can make a better guess on whether the issue is important to core customers or casual visitors, and adjust the message and offers appropriately.  To think all of this came out of trying to answer one simple question on newsletter response.

Having now discovered the secret of the Recency Chain, the owner was confident could be taken to the next level of profitability.  Things are sweet, the owner thinks, as she turns off the light and “heads home” – down the stairs to her living room.

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