Google Analytics is a popular tool for measuring marketing performance. The set-up seems easy. Create the Google Analytics (GA) account and plug it in onto your website. It seems to be free but it could cost retailers down the road. Here’s why relying on GA alone for digital marketing budget allocation could hurt your business.
How Google Analytics works?
There are four elements to Google Analytics: collection, processing, configuration and reporting. To learn more about it, read this article by Analytics Academy. The goal of these four steps is to give you a sense of who, what, when and where is happening on your website – be it an eCommerce site or not. You will see page load times, page views, unique visitors, goal/ commerce conversion rates… The reason you started Google Analytics in the first place was to get insights that allow for spend optimization. Where to spend next? Most likely, you hired a specialist to deal with all that data and tell you what to do. But here is what they found – the numbers don’t add up so they give you a conversion rate ranging from 1.3% – 19%. That’s better than plain guess! And it’s free.
But why do you think Google Analytics is free?
When you set up Google Analytics, you get a cap on number of visitors that can go through your website. When you get reports, you get sample numbers of X% of your actual traffic. To avoid cap and inaccurate measurements, there is an option of upgrading your service to a Premium level. Scott Matteson in his article for TechRepublic gives a nice summary – “the free version “samples” data, meaning you get more of an average of the statistics – you might not see the accurate hit count on a very busy website for instance. The premium version gives you more accurate results. The premium edition is available for a flat fee of $150K per year.
And what makes it so expensive for retailers!!
What makes it an expensive decision for retailers is that GA becomes the go-to guide for measuring marketing effectiveness. In the age of omni-channel, multi-path retail journeys, what you get from GA is just a sliver of information. There’s a reason the Titanic hit the iceberg. Consider the reasons why things could go horribly wrong.
- Seasonality and non-digital effect: If trusting GA (or Omniture for that matter), the bias is towards monitoring clicks versus sales. This totally discounts the seasonal effects like Back-to-school, Christmas etc. Seasonal impact has to be parsed out of the attribution modeling.
- Last click bias: Most companies opt for last click attribution (or first click). But there lies a big issue: you are passing all spend credit to the last/first click or the last/first referral. By default, this is what Google Analytics reports show. Both are terrible decisions. Why give all the credit to the last channel or campaign when there were multiple stops on the conversion path? That is inaccurate and also rises a threat of double paying. Retailer has already payed the affiliates, Google AdWords, for boosted Facebook post, but coronated only one of them as the main driver, pouring extra dollars towards that campaign. Similarly, “first click attribution is akin to giving my first girlfriend 100% of the credit for me marrying my wife.” This is the best comparison I’ve ever come upon!
- Modeling unknowns: Linear and Time decay attribution are better options than last click. Here is what Avinash Kaushik says about them: “When my son was smaller he would go to competitions (sports or IQ) and everyone would get a participation certificate. Life, it turns out, is not utopian. When there is a competition, someone gets a gold medal, someone gets a silver, and someone gets a bronze. Everyone else goes home a loser, motivated to work harder the next time and win.” The idea behind linear attribution model is that each touchpoint get equal credit, as in the time decay model, last touchpoint gets the biggest credit and each previous touchpoint gets less than the one after it. But the issue here is what is the window to be considered. Should the analyst park 7 days or 70? This decision will impact the future spending target and might impact the warmness of CMO’s chair!
- Impressions!: One more aspect to consider that Google Analytics does not and can not take into account – impact of impressions. What’s a Facebook like worth for a food brand that published a new recipe? What’s the impact of a video explaining the mattress design for a mattress company? Based on plain GA reports, these might slip the radar and cost big loss in revenue.
- Cross-device journeys are more and more becoming a norm. As of now, Google Analytics does not capture that. Same user is clicking potentially same ad on different devices but GA records them as unique visitors. Retailer is paying for every click, and allocating more money to that particular channel when it actually is not as attractive as the report suggests.
- Affiliates: Another question is the impact of affiliates. If you don’t tag them, they are lost. But paid nevertheless! And usually affiliate are the main reason for strained budget that adds up to 138%. That’s off, right?
- Fox guarding the chicken: And lastly – why put a media company in charge of measuring impact of its media. It’s same as having IRS doing your books 😉 Join the ranks of google skeptics – find tools that do not claim to be cheap but are not as expensive as free Google Analytics.
True cost of Google Analytics
Google is one of the largest media companies in the world. They monetize from every penny that they get from the keywords you bid on, display ads and what not. There ain’t no such thing as a free lunch here and everywhere. I find an explanation by Dave Collins (could call him a google-skeptic) of what lies behind “freeness” of Google Analytics quite accurate.
When a company purchases any form of advertising, they risk letting the company they’re advertising with know more than they should. But if Company A is letting Google see what their visitors click on, what they do when they get to the website, and how many of them then purchase, they are giving away everything. The prices that Google set are not random. They are based on demand and perceived value. Give them all the information and it can only work against you.
Obviously, the more access there is to customer behavior, the better targeting strategies can be applied. In this case, YOU are the customer of Google.
Still not convinced? Consider reading Google analytics and the case of the One-eyed horse.
And the game is on!
The Infernotions way : Multi-touch, multi-channel, algorithmic attribution
We model the shopping path as a stochastic process [so the engineers tell me] to put a weight to each channel according to its location on the shopping path in respect to the time to purchase and the channel touch points before and after that instance. In addition we factor in the effect of seasonality and singular events.
To learn more – see for yourself.