We all know that it’s extremely difficult to track sales through social media. The inability to cookie a user or prove the same person bought the product is a huge obstacle.
Instead, we tend to use signals to determine an estimated increase in sales. We look at metrics such as impressions, branding, page depth, and percentage increase of unaccounted sales, to try and help us gain some understand of the impact our social strategy is having.
However, in the recent growth of social media, it seems like we’ve lost track of what we really want to know; how much did I sell because of social media?
In hopes of making our tracking more succinct and meaningful, we must first determine what information is missing, and then find signals that may correlate with an increase in sales.
The Multiple Ways Social Media Influences Sales
Because social media effects the sales funnel in multiple locations, we should first outline where and when it may influence a sale.
The most problematic is the offline sale. Similar to billboards and TV ads, social media introduces a branding element that may convince a consumer to buy offline. This element is attributed to sales from brand familiarity and loyalty.
Imagine that every time you were online you saw Tide through their Facebook page, their blog, their twitter account. Conventional wisdom states that if you see a logo enough times, finally when you’re in the store, you’re subconsciously more likely to buy that product or brand.
Because of the jump from online to offline tracking the effects of online branding is extremely difficult.
Another way social media can effect sales is in the online impulse buy relationship. Tracking these visitors is simple because of the immediacy of the sale after interacting with the brand.
However, since much of social media interaction occurs on third party sites it can be difficult to accurately assign all sales because of the less sophisticated analytics available.
Similarly, the consumers that engages the brand online then buys the product after a few months, it’s difficult to track. Most cookies (visitor tags used by analytics software) expire after 90 days, meaning we can only attribute sales that occur online after engaging, for up to 90 days.
To add to the complexity, third party sites don’t allow brands to tag visitors, which makes it difficult to track these consumers.
Finally, social media play a major role in customer services, where it can influence return sales.
A consumer that buys a product and receives excellent service, is more likely to buy the same brand again. Tracking these return sales is difficult because they happy overtime, and don’t always occur wholly online (consumers may buy in stores after engaging online with customer service).
As you can see there are a number of obstacles to overcome. Tracking sales will not be an exact science, but by taking our calls from the offline world, we may be able to build a model or define metrics, that help use estimate the impact of our campaigns.
Social Media Metrics to Track
To wholly track a campaign we need to know and monitor a number of metrics. For straightforward sales optimization we need to know:
- total unique referrals from social media sites
- entrance path of all sales
- total impressions of social profiles
- determine size of community and average click through rate to site
Although these statistics may seem simple to track, but because of the fragmented nature of social media, we’re never privileged to all the information.
Consider, unique referrals and entrance paths. The fact that many Twitter users use a desktop application, makes it difficult to track referrals, since these Twitter users will show up as ‘direct traffic’. We can use bit.ly to estimate, but even that link may be passed to other social platforms, poisoning out results.
Also, because there is no single place to aggregate analytics information, it’s difficult and cumbersome to compare data. Right now, we can’t easily see shortened URL click throughs with referring URL and time on site. Time on site would have to come from a package like Google analytics, while click through came from bit.ly.
How to Find Social Media Metrics and Data
Interestingly enough, in my mind, to salve some of the dilemma we have, the best thing to do is introduce coupons. For most online and offline retailers, digital coupons are very simple to do and give a huge amount of information.
Whenever a brand needs to know an estimated reach and return of their social campaigns, they could push a small coupon (10% off, free shipping, or free gift with order). The coupon should only be available and promoted through social media.
After the coupon life-time has passed, we can begin using the data to gain an understanding of the community and their propensity to buy. Compiling the community size, by manually adding friend counts across social sites, allows us to determine the percentage sales from social media.
This number is simply an estimated and may be inflated or deflated based on the community. In the creation of the formula we assumed that the entire community was exposed to the coupon, which is more than likely not true. Also, we may see bleed through from consumers buying and using the coupon that they found online, not through a social interaction.
Conclusion
In the end, however, the number does give us an idea of what impact our social media campaigns have. It is not perfect, and until we are able to access full third-party analytics, exact referrer from applications, and perfectly match promoted content with sales; we won’t be able to exactly track sales. Instead, what we can hope to do is gather a snapshot of the success of our programs, and extrapolate the data.
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