A Four Factor Model for Assessing Social Media Performance photoOne thing most social media firms struggle with is assessing the market performance of their social media campaigns — often referred to as ROI.
I know I spent X on this social media marketing campaign, but what did I get back?
Over 70% of firms ask this question and only 15% of respondents in a Duke study felt they had a good handle on the ROI of their social media marketing. As a result, CFOs and others are tightening the screws to force a results-oriented approach to social media marketing.
In an era when IT specialists and BI (business intelligence) analysts decry the nearly 2 zettabytes of data available (the equivalent of 47 YEARS of HD TV programming), why do we still wonder WHERE’S THE ROI?
Too many tools, to few insights
In part, the problem is a function of TOO MUCH data — everything from # of Facebook likes, to unstructured Tweets, to # of shares.
How do you make sense of this variety of data coming at you so fast you can’t analyze it before more comes your way? The tendency, as I mentioned in an earlier post, is to pay attention to everything. But doing so, limits usable insights rather than expanding that horizon.
Too many tools exist claiming to provide insights to help determine your ROI. For instance, I’ve spent a lot of time with IBM and their social media intelligence product, CoreMetrics. CoreMetrics is a great tool for monitoring visitors as they move through your website on their way to buying (or not buying) your brand.
- Where did they come from?
- What did they do once they got to your website?
- What problems did they encounter trying to buy your products?
- Which social platforms brought a higher percentage of buyers? Which platform brought the biggest spenders to your site?
Very cool stuff and you DEFINITELY want to have this understanding. But, is that all there is to ROI? What happens BEFORE folks get to your site? What creates the awareness, interest, and desire that drive consumer buying (and by this we mean both end users and business consumers)? Aren’t these critical precursors to buying (rhetorical question — we KNOW they are)?
Down the rabbit hole
And, these questions lead us right down the rabbit hole — drowning in information we really can’t process into business insights. Do we measure likes or shares, or …? How do these translate into buying intentions? What DO we pay attention to? (Notice I’m very good at asking questions today and not giving you many answers (LOL).
The four factor model
I found a model that we might modify to help make sense of all this STUFF — data without a clear analysis plan. Originally, the four factor model of performance comes from sales force evaluation — where there are both input factors (such as # of sales calls, new leads) and output factors (like sales and AOS (average order size)).
The four factor model of sales performance looks like this (according to the Tanner, et al. Sales Management Book):
Sales = Days worked X call rate X batting average (close rate) X AOS
Coolness. We now have a model that helps us understand our sales volume. Improving any of these factors should improve our sales.
So, now let’s think about how we might build a model — four factors or otherwise — that helps explain social media performance! What factors do we include?
Well, let’s start by calculating amplification — the number of shares of our content since the more folks aware of our product, the more folks who might BUY our brand — I mean face it, you can’t buy something if you don’t know it exists. Maybe we weight this raw amplification number by something related to the content of the share. I did this for a client once and it worked out pretty well. For instance, you might weight content you created a 1 and content created by others a 2 (you can even make it loglinear by using a larger number so that your content is a 2 and others content about you is 2X2).
We now have part of our four factor model:
Sales = amplification (weighted or not)
We know folks won’t buy your brand if they think it’s crap (technical term there). So, we probably want some assessment of whether folks like your brand or not. Now, many tools simply use as much data as they can to score sentiment. I’m not crazy about this because you assume every positive (and every negative) is equal. But, we know the statement I don’t like X brand is no where near as negative as X brand sucks!, so why treat them the same. That’s a topic for another day. For today’s discussion, let’s assume we have some measure of sentiment we feel pretty good about. Let’s add that to our model:
sales = amplification X sentiment
We’re making progress.
Just like the sales person who impacts sales by making more sales calls, a brand impacts sales through its marketing intensity. Let’s add stuff like the CTR (click-through rate) for our online advertising and enewsletters + our media spend since folks don’t JUST live online + inducements like coupons or free product to the mix. For now, we’ll ignore the issue of scale for these elements, but we’ll work that out so each element is equal (or weighted as we see fit).
sales = amplification X sentiment X marketing intensity
Now we can add in our close rate. Using a tool like CoreMetrics, we can calculate the percentage of folks who buy based on number of unique visitors to our site. We can even fancy it up a little by separating new customers from return customers and/ or first time visitors from repeat visitors.
sales = amplification X sentiment X marketing intensity X close rate
We now have our four factors. Maybe we add more factors to our model. We’ll think about that.
- social graph
- network size
- actions by influencers
- … your ideas????????
Feel free to add some of your own in the comments below.
Using the four factor model
Now that we have our four factor model, what do we do with it?
Well, first off, we can now compare across social networks. Which networks provide the most sales?
We can experiment. What happens when we spend more to increase the marketing intensity? Modify the site to increase the close rate (taking a single click out of the sales process dramatically increases sales)? Or maybe we increase amplification by creating some content with killer headlines and images. What does that do to sales.
With these experiments complete, we now know WHERE to focus our social media budget to create the greatest impact on sales.
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