Six Good Reasons To Make Use of Predictive Analytics
This is the data that helps you see into the future.
Volumes of data continue to grow. Customers can be reached (and turned away) through a continuously growing range of channels. “Blast” marketing continues to fall out of favor due to the lack of offer relevancy.
The Solution: The growth of predictive analytics helps to address all of those needs, while the technology is keeping pace with demand. There is a natural progression of more adopters, lower costs of resources, and more momentum to reach out. Whether it’s gaining better understanding of changing market dynamics, or zeroing in on the best next message for every customer in your database, predictive analytics can deliver. If you’ve been entertaining the idea of predictive analytics, here are six good reasons to get on board:
1. Make better use of collected data. When you understand how customers are likely to react in real-time, you can start making more powerful strategic shifts in spending and messaging. Don’t run from high data volume. Gather it. Focus on quality, and let predictive analytics dive deeper to help model the ongoing customers responses to your marketing activity.
2. Evolve beyond RFM (recency, frequency, monetary) models. RFM models use a lightweight predictive component since they focus on messages which will move customers from their current standing up to the most recent purchase category. The most prominent limitation is that the data is always just a step behind, so they are taking data from only the past.
Predictive analytics can do much more. Instead of only focusing on the offers that have generated past sales, you can model the behaviors and exposures which will keep customers engaged and keep them from becoming inactive.
3. You will be able to understand customers better than your competitors. According to Forbes Insights data, only 15 percent of marketers use multi-channel/multi-touch attribution data in their predictive models, or approximately one-third as many as use website data and demographics. That’s your opportunity to create a competitive advantage.
4. Refine your understanding of customer lifetime value (CLV). Predictive analytics is particularly powerful for products that customers naturally grow out of, only that ‘outgrow’ date cannot be easily predicted. An infant formula brand, for instance, may lose a customer because a competitor’s brand has lured them away, or because the customer has no more small children requiring infant formula. Predictive analytics can help better model whether the relationship can be saved, or if it is time instead to transition the customer to a different set of products. You’ll want to continue to recommend the product if the model shows that the customer will retain an interest in it, but modeling will help you understand when you’ve exhausted the opportunity.
5. Understand how future shocks will impact your business. The inevitability of change and the possibility of disruption could be used as a superficial argument against developing models for predictive analytics. After a major shock to your business or industry, it may be assumed that the model is no longer of value – however;
While it’s true that models do require constant care and attention in order to keep them viable, that is actually a good thing. When the inevitable change does occur, predictive analytics can help you understand the potential impact of that change – and the range of potential outcomes. For example, if a new competitor is expected to take 15% of the market share, predictive analytics can help model where they will be likely to pull that share, and how your company should adjust spending for the future to compensate.
6. Take advantage of falling prices. Today, crunching ‘Big Data’ is more cost effective than ever, and very easy to deploy at will. Fierce competition among predictive analytics service providers and strategists is also making the discipline more accessible to a much wider range of companies. Where companies had to once house servers to collect data, which could prove very costly to maintain, now cloud services are readily available. The software and hardware are much more cost-effective, so even smaller companies can take advantage of the market.
49%: Marketers using predictive analytics today [Forrester on behalf of EverString]
40%: Marketers planning to implement predictive analytics over next 12 months [Forrester on behalf of EverString]
17%: Compound annual growth rate (CAGR) for predictive analytics market, 2015-2020 (Zion Research)
11%: Projected growth rate for “computer and information research scientist” jobs (data scientists), 2014-2024 [US Bureau of Labor Statistics]
About 3x: Chances a marketer uses web data or demographics in predictive analytics, compared to using multi-channel/multi-touch attribution data. [Forbes Insights on behalf of Lattice]