Stop over/under mailing your email list – Start adapting email frequency to customer value and online behavior

Email Marketing – a high ROI medium

Many marketers now view email as the best-performing channel in terms of ROI. Email ROI was expected to reach about $45 for every dollar spent in 2008, more than twice the ROI of other mediums (source: marketingcharts.com). In a recent survey from Datran Media, 8 in 10 marketers cited email as one of the strongest advertising channels of their company.

(source: Datran Media, Annual Survey 2009)

The real cost of email marketing: unsubscriptions and spam-complaints

Unquestionably, the distribution cost for emails is incredibly low when compared to other channels. One can email one million customers with less than $500. Reaching the same number of customers by mail would cost around half a million dollars (1000 times more).

Due to the cost of mailing, one of the most critical question direct-mail marketers try to answer is: “Is this customer worth being mailed?”. RFM models or advanced response modeling algorithms have to be used to make sure an acceptable ROI is obtained every time.

In the email marketing world, this question is almost meaningless. The entire customer file can be emailed again and again with a very limited budget. For most businesses, a response rate of 0.01% is sufficient to make the ROI positive.

The real cost of an email campaign is not the distribution cost. It is the loss of potential revenue associated to email unsubscriptions and spam-complaints.

Finding a balance between more revenue today and diminished revenue for tomorrow

The more you email a list, the more revenue you can obtain immediately. If you email customers too frequently, you risk spamming them. This could lead them to get on unsubscriptions lists and eventually turning them away from you.

Kirill Popov and Loren McDonald from EmailLabs tell the story in clickz.com of a multichannel retailer who increased its email frequency from 5 messages a month to 12 messages a month. The revenue increased by 38% but the unsubscriptions rate more than doubled from 0.74% to 1.77%. While the actual figures may vary from a business to another, those numbers are in line with what we found during our years of practice.

Getting the good (more revenue) without the bad (unsubscriptions): How to determine the optimal email frequency?

It is surprising to see that very few companies determine optimal email frequency using facts and sound analysis. Most of the time, marketers define an email frequency based on common industry practices, personal experience, and gut feeling.

Email frequency is too important to be left untested. One can even argue there isn’t one optimal frequency. Every customer is different and requires a different email contact strategy.

The approach we usually follow at Agilone is three-fold:

1. Build a 360 degree view of customers with sales data, web data, and email data
2. Segment the email list based on customer potential value and email/web activity
3. Determine the optimal frequency for each segment by using robust A/B testing techniques and a few formulas

Building a 360 degree view of customers with sales data, web data, and email data

The more we know about a customer, the better we are able to target and increase the relevance of our marketing efforts.

At Agilone, we have the technology and expertise to connect to various systems such as order management systems, web analytics solutions (Coremetrics, Omniture, etc.), email marketing solutions (Silverpop, ExactTarget, Responsys, etc). Connecting these dots, we build a robust foundation for all subsequent analytical work

Customer segmentation: Mailing more to high-potential highly-engaged customers

Different customers require different email frequencies. Agilone has defined two dimensions on which customers can be evaluated.

• Potential value: how much future revenue is expected from a customer/prospect
• Web/email level of activity: how responsive/engaged is a customer with regard to web and email activity (how often does the customer check the website, how many emails did they open, etc.)

The underlying idea here is that we should email more frequently the customers with high potential value, and who do open all the emails we send, check our website regularly and even forward our emails to friends.

Using advanced statistical modeling, we give a composite score to all customers and prospects. The entire email list is segmented into 10 deciles that we can treat differently.

The top deciles show the best response rates and the lowest unsubscriptions rates when compared to the other deciles, for a same email frequency.

Email Frequency: testing, testing, testing…

Different frequencies can be tested within each of the segments defined above. Let’s say that for a particular segment, we divide the list in two. One half of the segment receives 8 emails per month (let’s call this list A). The second half receives 12 emails per month (list B). As one can expect, we get more revenue from list B but the unsubscriptions rate is lower for list A.

Which frequency should be preferred? Let’s consider the following formula.

Additional profit from higher email frequency =

+  Increase in revenue attributed to higher email frequency

–   Additional distribution costs due to higher email frequency

–   Additional creative costs due to higher email frequency

–   Future revenue loss because of additional unsubscriptions (nb additional unsubscriptions x (future revenue from active email – future revenue from unsubscribed email))

This formula and its various components can produce some great insights.

We will give more examples and details on the three stages above very soon.

I hope you have enjoyed the article. Please continue the discussion by posting your comments here or by shooting an email at info@agilone.com

Read you soon!

Anselme LE VAN

Associate, Analytics | Agilone

Website: www.agilone.com

Harvesting the full potential of email marketing through real analytics – Intro

Email marketing is becoming one of the most preferred marketing tools for communicating and developing relationships with customers.

According to a Jupiter Research forecast, spending on email marketing will reach $1.1 billion in 2010 from $885 million in 2005. In the US, marketing email volumes is expected to reach 838 billion marketing messages in 2013 (source: Forrester 2008).

Email marketing is indeed a powerful way to reach out to customers. More than one-third of internet users say they check their email throughout the day. (source: AOL/Beta Research Corporation, June 2008).

Listening to marketers, I often feel that email marketing is appreciated for three main reasons:

• LOW COST: while direct mail requires significant marketing budgets, email campaigns can be executed at very low costs. Low costs allows marketers to promote to their entire customer and prospect database (instead of picking up consumers deemed worth a mailing). Also, marketers can execute campaigns much more frequently (an email sent once day a common practice among retailers).

• EASY ROI CALCULATION: Estimating the realistic ROI of an offline add campaign has always been a tricky issue for marketers. Calculating the ROI of email campaign is easy, simple and automated.

• SHORT-DELIVERY TIME: we often find that the lead time of a catalog mailing campaign is about 3 months from kick-off meeting to mailing execution date. The lightness of email marketing makes marketers more responsive to purchasing trends. Also a real-time relationship with customers can be engaged with triggered-campaign such as welcome campaigns, abandoned-product campaigns, etc.

Most marketers leverage those three items in a satisfactory manner. However,  we have proven at Agilone that email marketing can do much more. The points listed above are the bare improvement of something that existed before with “traditional” marketing tools.

Taking a broader view, we are convinced Digital marketing heralds the advent of a new marketing paradigm. A new era abundantly researched by scholars where mass marketing is superseded by a personalized uninterrupted relationship between company and consumers. In this new paradigm, marketers personalize their message, offer, timing, and touch strategy for every single consumer relying on the insights produced by advanced analytics techniques.

Email marketing is one of those tools that pave the way for this new era.  However it is surprising to find that it takes quite a long time for marketers to embrace the new possibilities offered by this technology:

• COLLECTING AND ANALYSING CUSTOMER BEHAVIOR DATA: Like most digital channels, it is now possible to record all customer interactions in an automated and affordable fashion (who open/read their email, how any times, when, what product do they look at, what links do they click on, email forward behavior,…). Tracking and analyzing customer behavior generate great insights and actionable knowledge. For instance, we found that online behavior is a very important predictor of off-line behavior. More details will come on this later on.

• OFFER PERSONNALIZATION: since the beginning of times, marketers have tried to create meaningful segmentations in order to, among other things, better promote to specific groups of customers. However, the marketing tools available at this time did not allow them to use their segmentations in a very practical manner. For instance, in the catalog business, it is hard to have more than two or three covers and the contact strategy is often resumed to “who should we mail the catalog to?”. With emails, it is now possible to create a personalized offer for every single consumer based on its profile and past behavior. Agilone has developed such tools and can integrate easily to virtually any email marketing solutions (ExactTarget, ConstantContact, SilverPop, etc..). Again, more is to come on this shortly!

• INTENSIVE TESTING: The ability to track customer behavior and personalize promotions is all marketers need to test, test and test. While testing can be a heavy process in offline marketing, it is very easy now to send different offers, try various layouts or test a variery of touch frequency, etc..

Agilone has developed an impressive expertise and set of capabilities in those areas. We will slowly share our little secrets in the weeks to come. Stay tuned!

Anselme LE VAN

Associate, Analytics | Agilone

Website: www.agilone.com

Ways to enhance response modeling: model the household not the customer

Response Modeling If you run a catalog based retail business – that is, if you are heavily dependent on regular catalog mailers for reaching out to your customers, good response modeling is crucial to cutting marketing costs and enhancing return on your marketing dollars. It is even more crucial to targeting the more responsive customers – enabling you to improve your ROI.

The philosophy is simple: mail the catalogs to people most likely to come back and make a purchase. This way you make the most revenue while mailing the least catalogs.

Getting good at it! There are several mathematical and statistical techniques that can be employed to make a good response model. But even before you start employing fancy math, there’s a lot you can do at the data collection step itself that can improve your modeling. You can sit down and find the most pertinent variables to be considered for the model, for instance.

But an even easier trick is to not model response by customer, but model response by household.

Multiple customers at each household Often, retail businesses attract more than one customer per household. For instance, husband, wife and daughter might have all bought apparel from your catalog – three customers from the same catalog. The database might have even recorded two/three customers when there is really just one – sometimes in their haste to record a sale, the sales rep would have assigned the customer a new customer id. So this new customer might have bought 5 items under Customer ID 1, 3 items under Customer ID 2 and 1 item under Customer ID 3. Really, he’s bought 9 items.

But even if that is not the case, and you happen to be modeling response by customer not household, the following might happen: Husband, wife, daughter all placed 2 orders each – and hence any customer based model might not give any of them a high rank. But the household – has 6 orders! The household itself will get a higher rank in the model, as it should. Someone from the household will make a purchase!

Testing time! In order to test this theory out, we made a customer based response model, then using the same data – first rolled up variables to the household level – then, made a household level response model. We had about 1.4M households to contend with and we ranked each customer/household on its propensity to return and make a purchase. We then ordered these ranks in 100K segments and found out how many customers had returned per segment.

Results Both models ordered the file really well – at 50% of the file, both models had more than 90% of the response coming in. However the house hold based model comfortably beat the customer based one. The graph shows the % of response captured per the each 100K ranks of the two models.

So at 700K, for instance, the customer based model captured 92% of the total responders while the household based model captured 95% of the total response. The usual catalog circulation size for this client was 700K and this improved model would have brought in an additional 3% of the responders for the same number of catalogs mailed. This would have equated to roughly $77K per month – an amount of no insignificance.

Or looking at it another way, the new model would have brought in the same amount of money by mailing 100K lesser catalogs (since the new model brings in the 92% response at the 600K mark). At the cost of 70 cents a catalog, that’s a saving of $70K to earn the same revenue as before.

Modeling by household thus provides a definite edge over modeling by customers. Not only is it a win in monetary savings, or increase revenue through your catalog mailing, it also ensures you send out one catalog – for each household. This way you get that one catalog for getting a sale from any or all of the possible customers you have there.

Dhruv Bhargava

Manager Analytics| Agilone

Website: www.agilone.com

Blog: https://agilone.wordpress.com