Are you advertising on Facebook and not sure how to integrate ROAS into your cash flow forecast?

Facebook is an incredibly powerful tool to power your business, particularly when it comes to reaching large numbers of target customer prospectsthat resemble your existing set of loyal buyers. However, challenges can emerge in many areas. One of the most vexing challenges is being a victim of your own success! Too much success with a Facebook campaign can rapidly exhaust current levels of inventory. Depending on the length of your supply chain and restocking ability, it can be a real challenge to profitably grow your company while staggering from one large inventory purchase to the next. Facebook is also a fickle advertising marketplace. While today's buyers continue to purchase tomorrow? How can the risk of fading trends and fads be modelled and understood? 

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We believe the best approach to these challenges is a comprehensive business intelligence and financial forecasting function. Prerequisites include high quality monthly bookkeeping, and a strong relational database functionality connecting the disparate parts of your business - from warehousing, inventory, cash flow and predictive analytics capability - to name just a few. Success is possible, but it takes an investment in capacity building up front.

Attribution analysis is the key to understanding that all important "Return on Advertising Spend" ratio. While Facebook's business performance tools are quite powerful, there are often gaps that may exist with respect to campaign setup and understanding the data that is being generated. Moreover, it is often difficult to understand and independently verify how Facebook is coming up with its ROAS numbers. Even if transparency and data integrity were not issues, there can be real challenges to understand full attribution in all its complexity. For example, imagine that a consumer is shown a Facebook ad eight times over eight weeks. Finally, they make a purchase. They do so without clicking on a Facebook ad prior to the purchase, however. How can we understand the ROAS that applies in this instance? How much did each of the ad exposures influence that final purchase decision? 

• Attribution analysis is complex, and perfection may be impossible in many circumstances
• Feedback loops and A/B testing are powerful concepts to understand and use to your benefit
• Multiple data ecosystems often need to share data into a centralized repository before full attribution can be properly analyzed
• We can't often run perfect experiments or controlled trials to separate independent and dependent variables - a certain amount of estimation and error is part of the process

Often eCommerce entrepreneurs are interested in high ROAS numbers right off the bat. However, this can be a dangerous approach and lead to volatile sales performance. Consumers may fall in and out of favour with a particular product rather quickly, and without a roster of "lower ROAS" inventory items that are percolating at all times, a company can be left with a collapsed sales funnel. Overreliance on a few best sellers is dangerous.

• Consumers often require multiple exposures and ample impressions before choosing to buy. 5x or more is common.
• A strong support network helps to reinforce positive feedback loops. A Facebook group with a lively community of users can be a great ally to a sales campaign.
• Understanding preference drivers can help entrepreneurs anticipating changes in tastes. Seasonal demand drivers are often critical to understand.