Predictive marketing is the practice of anticipating your prospects’ next moves in order to improve customer journeys, increase satisfaction, and to build sustainable, profitable revenue streams. For example, optimized cross selling, content personalization, message timing, and ad effectiveness measurement demand accurate predictive analyses. The advent of practical AI and machine learning has made these tasks feasible for most organizations.
So why has adoption of AI and machine learning for marketing been slow, especially in the mid- to small-size companies? Several reasons are often cited*:
*Source “AI for Retail Marketers USPS Research,” USPS & SIS International, June 2019.
Why do these roadblocks still exist when there are a number of cost efficient, versatile and ease to use solutions available to immediately breakdown these impediments? Let’s address each separately:
57% of respondents said allocating budget was a challenge
Budget allocation is a perennial challenge, and always a poor excuse for inaction. After all, if a new technique delivers better results with demonstrable ROI, who would object? Reasonable business people don’t. The real budget objections are perceptions that:
All three of those points had been obstacles, but aren’t any longer. Low-cost tools are available that not only offer the flexibility to answer a large range of marketing questions, but also can be implemented—without data scientists or programmers—in hours vs. weeks. The versatility of these tools allow organizations to equip multiple analysts to make action-oriented predictions easily.
It is time to revisit budgets. Efficiency can be gained nearly instantly within the resources of even small companies.
48% of respondents said getting upper management on board was a challenge
This one is easy to understand. Management teams have been promised results that failed to materialized after large projects and massive expenditures. Why will it be different this time?
Fortunately, this obstacle is simple to overcome. Automated machine learning makes attacking a proof-of-concept straightforward and fast. Nothing speaks truth like results.
43% of respondents said training and recruiting staff with the right skills was a challenge
This one is also based on old information. Solutions built to provide predictive marketing power to analysts are specifically designed with them in mind. That means there is no recruiting. Existing teams can now act on forward-looking data without writing a line of code. It is as simple as using a spreadsheet. Training expenses are minimal for applications that are self-service for anyone with basic analytical skills.
39% of respondents said knowing where to start was a challenge
Starting points can be difficult to find when processes are complicated. The first step is always “learn the process.” For approaches that require data science knowledge and programming skill, there is no simple path for analysts who do not have legions of specialists available. Once the process becomes as straightforward as uploading a few data sets and inspecting results minutes later, experimentation becomes an automatic starting point.
Knowledge gained in the first few runs reveals whether data is sufficiently broad and deep, if the predictive question is appropriate, and how the results can be translated to actions. Incremental tries with tweaked data and assumptions are fast, which helps analysts converge on a solution quickly. Low overhead means refinements are low effort and cost.
The take-away is that misconceptions are the reasons many marketing analysts don’t use AI and machine learning for predicting critically valuable questions. There really is nothing stopping analysts from generating predictive actions with today’s SaaS solutions.
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