A recent article in the Harvard Business Review on Advertising Analytics 2.0 shows how advanced analytic tools and concepts can improve the return from your growth efforts. The article, written by Wes Nichols of MarketShare, shows how ad channels increasingly interact with each other and you can be much more effective by understanding these interactions. What you do in performance marketing, search ads, web, YouTube, TV and PR are not independent of each other. For example, a TV advertisement may increase Google searches that are then directed to your web game by purchasing ad words.
Advanced analytics allow you to understand these interdependencies and allocate accordingly. For example, one company found 85 percent of its budget went to TV ads and six percent to YouTube ads but the YouTube ads were nearly twice as effective at driving search. They then changed their allocation of ad dollars. This adjustment increased sales nine percent without incurring any additional advertising expense.
One of the keys to using analytics more effectively is understanding what data to collect. Many in the game industry think that tracking clicks on cost-per-click (CPC) campaigns, adding some consumer surveys, focus groups and last-click attribution is enough to optimize their marketing. It is not. These techniques are both backward looking and treat each marketing channel largely independently. Also, companies are often comparing apples to oranges, measuring different strategies with different metrics. It also leads to double counting, as you might attribute the same monetization to two or three different channels.
As there is an incredible amount of data available (from web cookies to social media activity to credit card receipts to DVRs), the opportunity and challenge is using this data to understand the effectiveness of your advertising. The article quotes Nate Silver ( I have written about Silver in the past) as saying, “Every day, three times per second, we produce the equivalent amount of data that the Library of Congress has in its entire print collection.” Much of the analytics being used in the mobile and social game space, however, is backward looking and outdated; they largely correlate monetization and lifetime value (LTV) with a few variables. The opportunity is to use Analytics 2.0 to drive your marketing.
Powered by the integration of big data, cloud computing and new analytical methods, Analytics 2.0 provides fundamentally new insights into marketing’s effect on revenue. It involves three activities: attribution, the process of quantifying the impact of each element of advertising; optimization or “war gaming” by using predictive analytics tools to run scenarios for business planning; and allocation, the real-time redistribution of resources across marketing activities according to optimization scenario.
To determine how your advertising activities interact to drive purchases, start by gathering the right data. Knowing what to focus on, the signal rather than the noise, is a critical part of the process. To model accurately your businesses, you must collect data across five broad categories
- Market conditions
- Competitive activities
- Marketing actions
- Consumer response
- Business outcomes
With detailed data that parse product sales and advertising metrics by medium and location, sophisticated analytics can reveal the impact of marketing activities across each other (i.e. between click-through rate on mobile performance ads and social media activity). These indirect effects are called assist rates. For example, mobile ad click-throughs may increase (and thus become cheaper) after an article about your game in the New York Times. This technique would help you capture the article’s assist to the mobile ads and provide a truer picture of the public relations ROI. You need to make sure, though, to deduct the assist from public relations from the ROI of the mobile advertising, or else you would be double counting the return.
Optimization / “War Gaming”
Once you have quantified the relative contribution of each component of marketing activities and the influence of important outside factors, war gaming is the next step. It involves using predictive analytics tools to run scenarios for business planning. Working with the vast quantities of data collected and analyzed through the attribution process, you can assign an elasticity (defined as the ratio of the percentage change in one variable to the percentage change in another) to every business driver you have measured, from performance advertising to search ads to viral calls. With appropriate tools, you can evaluate thousands of scenarios quickly.
Integration with LTV
This is also the phase in which you juxtapose the optimization/war gaming analysis with your calculations of customer lifetime value by segment/cohort/source (as I wrote about last week). You need to see how changes in your marketing mix affect the type of customers you acquire and retain to get a true understanding of the best allocation of marketing resources. The R (return) in ROI that you are optimizing is directly tied to optimizing the delta between cost per acquisition (CPA) to LTV, keeping in mind that the CPA is a blended number based on the attribution analysis and LTV is different for different players.
All of the analysis above is worthless if you do not use it to allocate your marketing resources. You should adjust your marketing mix almost instantaneously (there is very little long-lead marketing in the social and mobile gaming space; even PR can be turned on/off within days). Allocation involves putting the results of your attribution and war-gaming efforts into the market, measuring outcomes, validating models, making adjustments and then starting the process over.
Using Analytics 2.0 to increase your profitability
This analysis allows you to have much greater visibility into what is working, how it is working and how you can improve your user acquisition efforts. Too often in the social and mobile game industry, user acquisition people discount everything other than performance marketing because they take a silo approach and effectively only measure the clicks on ads. Given the benefits many other industries have seen from implementing an Analytics 2.0 approach to marketing, it suggests that these tools can help game companies improve their return on investment, be it ad dollars or even platform decisions.