Business is tough and competition is brutal in a world where the internet expands your competition to every corner of the world. With many economies still sluggish after the financial Armageddon that caused a mortgage meltdown and the stock market to experience the biggest losses since the Great Depression, and the economic fallout caused by the global pandemic, it’s never been harder to make a buck. Businesses trying to gain whatever competitive edge they can, turned to big data and predictive analytics hoping to “exploit new opportunities and gain the upper hand over competitors” according to TechRadar. Yet, many fell for the myths about predictive analytics, which limited their ability to use data in ways that supported decision-making and, ultimately, improved their market performance.
In their interview with James Fisher, SAP’s VP of Marketing and Analytics, Fisher talks about the importance of predictive analytics for driving business success:
Predictive analytics technology is the core enabler of big data, allowing businesses to use historical data, combined with customer insight, to predict future events. This could be anything from anticipating customer needs, forecasting wider market trends or managing risk, which in turn offer a competitive advantage, the ability to drive new opportunities and ultimately increase revenue.
What is predictive analytics?
The graphic above does a good job of visually depicting predictive analytics.
As the name implies, predictive analytics use historical data to predict future events and guide decision-making. With more data, predictions may get better for a bunch of statistical reasons related to the normal distribution and robustness of individual statistics.
Let’s take a look at an example to see how predictive analytics works.
Predictive analytics: an example
Businesses face any number of decisions and their choices greatly impact the business’s market performance and, ultimately, the success of the venture. So, let’s start with a typical decision faced by most businesses: forecasting sales.
Your sales forecast is the starting point for financial planning and impacts almost every aspect of your business from hiring and scheduling sufficient numbers of employees to handle demand, buying raw materials and component parts to manufacture your products. Plan for too much sales volume and your bottom line suffers from too much inventory of both inputs and finished goods and your labor costs are too high. But, plan for too little production means you experience a high opportunity cost for sales you lost because you didn’t have enough product. Even though opportunity costs don’t show up on your income statement, losing sales can doom your business to failure. For this reason, some financial experts argue this intangible cost should show up on your income statement to guide better decisions.
Crafting a sales forecast
As noted in the infographic, forecasting sales involves not just predicting how much product you’ll sell in an upcoming period, it involves forecasting who will buy, where to ship product to reach these buyers, why they’re buying, etc.
Sometimes firms build up forecasts by asking their sales team how much each expects to sell next year but, as you can imagine, salespeople have a vested interest in low-balling sales since often their compensation is greater if they exceed their quota. Other times, businesses ask managers to project sales into the future, which often involves a simple extension of the existing trend line into a future period. None of these methods adequately forecast sales.
Build a predictive model
Instead, use historical data to build a sales forecast, which is much more accurate, although related at a superficial level, with an extension of your trend line. Here’s how to build a predictive model to forecast sales using regression analysis, a common tool for building an algorithm since it determines influential variables that impact sales and how much each variable contributes to your sales forecast.
- Gather historical data from internal sources, such as your customer database, inventory levels, planned promotions, and a variety of other analytics related to sales and marketing, especially results from prior promotional events.
- Use a data mining tool to build an algorithm using historical data and sales results for prior periods. An algorithm is a fancy name for a regression equation showing the relationship between your controllable variables, ie. promotions, and your sales.
- Use the model to predict future sales by multiplying the calculated weight and your anticipated level of each variable in the equation.
Myths about predictive analytics
No wonder more businesses use predictive analytics today than ever before. Not only is there more data available since businesses measure and digitize their data, but the cost of tools to analyze big data puts them within reach for most businesses [check out predictive analytics solutions such as IBM’s SPSS Modeler, SAS Enterprise Miner, SAP Predictive Analytics, and Oracle’s Data Mining ODM]. In the past, you’d need lots of expensive storage, massive computers, and expensive software to run predictive analytics. With the reasonable cost of cloud storage, for instance, AWS, and less expensive software that runs on the average desktop computer, everyone can harness the power of predictive analytics.
It turns out, even though more businesses try to make sense of their data, few succeed. Although a recent study by SAP finds more than 85% of surveyed businesses use predictive analytics and 77% believe they’re getting higher revenues because they’re data-driven, it turns out most are leaving a lot of money on the table — at least figuratively.
So, why are businesses large and small failing with predictive analytics?
Maybe they just drank the Kool-Aid and fell prey to the many myths about predictive analytics.
Now, don’t get me wrong. Anyone who’s read this blog knows I’m a strong advocate of data-driven marketing (and business intelligence, in general). The problem comes when businesses fail to understand how to do predictive analytics the RIGHT way.
Are you falling for any of these 10 myths about predictive analytics?
Myth #1: Predictive analytics is easy
Sure, new tools, like the ones listed above, make it easy to analyze big data and derive “answers”. In fact, you can throw in data and basically just let the machine run until it spits out something. The problem is, the answers might not be worth the energy it took to make the calculations.
Running analytics programs is easy, deriving the right answers, statistically, is not.
That’s because you can’t just turn the computers loose a let them run. Predictive analytics requires some serious training in consumer behavior (at least within the marketing area) as well as alignment with company goals.
I remember when I took a class from IBM on using their software. Within a couple of hours, we figured out how to run data and the different options for analysis. Then, they turned us loose on a real data set and I remember staring at the screen, not knowing what to do next. That’s because I didn’t have a theory about how the data might be related — a necessary starting point for running predictive analytics effectively.
Plus, you need a good understanding of statistics and know what you’re looking at in the results. For instance, what’s the R-squared of the equation. R-squared assesses the impact of your algorithm on sales and ranges between 0 and 1. A higher R-squared means your algorithm does a good job of predicting sales while the opposite is true for lower values of R-squared. In effect, R-squared assesses the percentage of variance in sales predicted by your algorithm.
Here’s a simple linear model where you’re testing whether high school GPA (variable) impacts college GPA (dependent variable). The model shows a number of points don’t fall on the line, suggesting a low R-squared. We can improve our model by adding other variables that impact college GPA, such as major, number of work hours per week, etc.
Myth #2: Scientific evidence is proof
Just because folks say something, doesn’t mean it’s true.
A great example is the New Coke debacle. Coke’s market research folks went out and asked consumers about their preferences for soft drinks. Coke used their responses to develop a formula that better-matched preferences.
People were in the streets protesting New Coke (called simply Coke).
People hoarded (Old) Coke so stores quickly ran out of their stock.
It was a public relations nightmare — or maybe not considering the $millions in free publicity Coke got.
Why did New Coke fail?
Simple. Folks didn’t just buy their product for the taste. They bought it for the whole brand image — the nostalgia of having grown up drinking Coke.
Coke never asked them about that. They never thought about it. They didn’t really understand their customers. Hence, putting the wrong variables into the equation leads you to make bad decisions.
Myth #3: Only what you can measure matters
Predictive analytics relies on metrics — many of them reflecting historical company data, some from research studies and others from external sources. There’s the prevailing notion that things only matter if you can measure them.
But, that’s not the case.
Sometimes things you can’t measure make a whole lot of difference.
Take trust, for example. Does trust impact whether folks buy your stuff? You better believe it does.
Maybe you can infer trust because someone buys your product, but you can’t directly measure it. So, it doesn’t show up in your predictions despite its critical impact on sales.
Myth #4:Correlation = causation
Predictions are primarily based on correlations (relationships) between the data you have.
But, correlations don’t mean that one factor CAUSED the other factor. Just because 2 things are related doesn’t mean one caused the other.
The best example of this is the correlation between hem lengths and the stock market — the shorter women’s skirts, the higher the stock market. Now, it’s an interesting phenomenon that the two variables are even related. But hem lengths don’t cause stocks to go up any more than high stock prices force skirts up. In fact, both are caused by confidence and a sense of well-being. Higher confidence boosts stock prices (as well as a number of other financial results) as well as raising hem lengths as consumers feel better about the world, in general. So, if you try to manipulate stock prices by forcing manufacturers to shorten skirt lengths, you’ll fail and the same is true for a number of variables that make it into your algorithm.
And, bigger datasets often generate more correlations that have no real meaning. We call these spurious correlations. With enough data, it’s hard to separate the trees to see which variables truly impact your dependant variable.
Myth #5: Predictions are perfect
Predictive analytics produce probabilistic estimates of the future. No one has a crystal ball that predicts with complete accuracy. Take horse racing for example. People, knowledgeable people, place bets on horses using predictive factors like age, bloodlines, prior performance … The odds reflect the combined predictions of all betters. Most of the time, the odds on favorite wins — performs as predicted. But, every once in a while, the long shot surprises everyone and takes the purse.
Returning to our sales forecast, this year put the proof in the statement that predictions aren’t perfect. With the global pandemic, unexpected changes that underpin your performance occurred and greatly impacted your results. Businesses like Amazon and FedEx excelled in a world where people couldn’t or wouldn’t leave their homes while more physical retailers saw sales plummet as they couldn’t spin up their online efforts fast enough to compensate for lost traffic to their stores.
This leads to the next myth….
Myth #6: Predictions are forever
Not so, as we saw with our horse race. More data usually results in better predictions. As time goes on and you add new data into your model your predictions get better, assuming no unexpected changes intervene.
But, sometimes, the whole model goes haywire. Cultural shifts, demographic changes, and other events might drastically change the model.
Of course, by monitoring events outside your firm, including those mentioned above, you add the impact of these variables on your forecast and still have a viable model.
Myth #7: You need a skilled consultant to implement predictive analytics
Not so. Go back to Myth #1 and you see the skill necessary to run accurate predictions. But, hiring an outsider might not be the best way to step up your predictive analytics program. Predictive modeling requires an intimate understanding of what data is available or can be collected, the goals of the organization, insights about the organization’s culture, market structure, and marketing plans.
Outsiders rarely have the internal knowledge necessary to run an effective predictive analytics program. They don’t have tacit knowledge of how consumers respond to your marketing efforts and they don’t have access to important factors impacting your sales that aren’t easily measured, like trust. Instead, you might have to invest in hiring or training employees to make your predictive analytics work.
Myth #8: Predictive analytics is mostly a machine problem
Somewhat related to some earlier myths is the notion that predictive analytics is a black box. You pour data in and something happens in the box (computer) that yields accurate predictions. It’s an appealing notion, but not completely accurate. Pouring in data often generates a lot of spurious correlations that don’t really mean there’s a relationship between factors, as mentioned above.
That said, sometimes pouring in data and seeing what comes out is an effective FIRST step in predictive analytics. However, the resulting predictions must be validated before a company uses them for planning.
Myth #9: Predictive analytics are expensive
As I mentioned earlier, predictive analytics doesn’t have to break the bank. New software and cloud storage make software to run predictive analytics within reach of most businesses.
Myth #10: Insights = action
This may be the granddaddy of all myths about predictive analytics.
Predictive analytics, done effectively, produce insights. But, unlike a pregnancy test, the results aren’t an answer. Instead, managers must struggle to make sense of the data, to parse results into meaningful segments.
For instance, building a predictive model may show which messages resonated better, thus generating higher sales. However, if you parse that data by customer segments, your insights are deeper and your actions clearer. For instance, in the graphic below (from Google Analytics), I see that most of my website visits come from Google and YouTube (also Google). But, I don’t have any insights. Instead, I need to investigate further to see which pages/ video bring the most traffic to my site, what time of day should I post content to drive the most visits, how did visitors find me on Google or YouTube, and, really important for my success, which types of visitors converted. If visitors from YouTube convert 3X better than Google, that’s where I put my money.
Turning those insights into action takes both intuition and managerial skill to gain buy-in from stakeholders and pivot the organization.
So, these are my 10 myths about predictive analytics to help you as you, hopefully, continue on your journey toward becoming more data-driven and using data to effectively guide your decisions.
I’d love to hear your thoughts on this post or how you’re using predictive analytics in your own business. Or, if you face challenges becoming more data-driven. Post your successes and questions in the comments below. Also, if you have ideas for future posts, please share them and I’ll work those topics into my editorial calendar.
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