Everyone’s talking about customer engagement. But that engagement takes many forms, including content consumption, website page views, email opens, paid and organic search clicks, call center interactions, likes and follows, tweets and retweets, and referrals.
While each of these things can be measured independently, taking a more holistic approach to measuring engagement can pay big dividends when it comes to optimizing your marketing budget.
In fact, many marketers are so obsessed with measuring things at the most granular level that they often miss the big picture. This is particularly true of direct marketers who are good at counting responses and transactions but not so good at understanding the effect that their efforts have on the non-responders—those who are exposed to their promotions and buy but can’t be sourced to a specific tactic.
Because multichannel consumers engage with brands on a variety of platforms, it’s important to develop a way to quantify the entire customer experience rather than just measuring and making judgments about specific pieces of it in isolation. That kind of one-off measurement can discount tactics that may have contributed to a sale in ways that aren’t directly measurable. Various attribution models look to give credit where credit is due in the online space, but even the most complex models don’t provide that 360-degree view of a customer’s engagement with the brand.
So where do you start? First, accept the fact that this isn’t a model that you’re going to build overnight. It’s an iterative process that will require careful measurement over time using different control cells to verify your assumptions—and you’ll be making a lot of assumptions at first.
Start by assigning a point value to every customer touch and action to create an engagement score for each customer. This process will be different for every marketer and will vary according to your customer base and promotion mix. For the sake of describing the methodology, consider the arbitrary assignments in the following table:
|
Activity |
Points |
|
Social Media Activity |
|
|
Facebook like |
1 |
|
Twitter follow |
1 |
|
Tweet |
5 |
|
Retweet |
5 |
|
Direct mail/email |
|
|
Direct mail solicitation |
2 |
|
Direct mail response |
5 |
|
Email solicitation |
1 |
|
Email open |
3 |
|
Email click-through |
5 |
|
Web Activity |
|
|
Website visit |
10 |
|
Registration at website |
20 |
|
Email subscription |
20 |
|
Personal Interactions |
|
|
Loyalty program enrollment |
20 |
|
Tell-a-friend referral |
10 |
|
Completed outbound call |
10 |
|
Inbound call |
10 |
|
Traditional RFM Measures |
|
|
Lifetime sales |
10 to 25 |
|
Last 12 months sales |
10 to 25 |
|
Last month sales |
10 to 25 |
|
Frequency of contact |
10 to 25 |
|
Recency of last contact |
0 to 25 |
Next, perform this preliminary analysis:
1. Rank your customers on sales volume for different time periods; e.g., previous month, quarter, year
2. Rank your customers on their engagement score for the same periods
3. Examine the correlation between sales and engagement; how much is each point of engagement worth in sales?
After you’ve done this preliminary scoring, try to isolate customers who weren’t exposed to specific elements of the promotion mix into control groups (e.g., they didn’t engage on Facebook or didn’t receive email). Compare their revenue against the rest of the file to see how well you’ve weighted that particular element. With several iterations of this process over time, you’ll be able to place a dollar value on each engagement score and plan your promotion mix to maximize it for each customer.
How you assign your point values may seem arbitrary at first, but you’ll need to work through this iteratively, looking at control cells wherever you can isolate them. For a more scientific approach, run a regression analysis on the customer file with revenue as the dependent variable and the number and types of touches as the independent variables. The more complete your customer contact data is, the lower your p value and the more descriptive the regression will be in identifying the contribution of each element.
The touchpoint that’s hardest to quantify is probably the Facebook like. Its value is much debated, and ranges go from more than $200 for nonprofits, as reported by NPEngage, to $0, according to Forrester’s Augie Ray, who says that a like only has value if you do something with it. So, the definitive answer on what a like is worth: It depends. And you have to figure it out for your own particular situation. Booz and Company tried to quantify it using likes per million of revenue; its report noted that WalMart with 34 million likes lagged far behind Burberry with 17 million likes on this measure.
Another consideration is “how did you get those likes”—organically or through an incentive promotion? A good place to start for valuing your social media is Value of a Like. HubSpot’s Dan Zarrella, who wrote about it in the Harvard Business Review and drew a combination of praise and criticism, developed this method.
As with any methodology this is only as good as the data you’re able to put into it, but don’t be discouraged if your data isn’t perfect or complete. Even in an imperfect world this exercise will get you closer to a more holistic view of your customer engagement.
Chuck McLeester is owner of Measured Marketing LLC
