EDITOR’S NOTE: Because extended enterprise learning involves multiple disciplines, we sometimes ask other experts to share their insights. Today we feature advice from Daniel Newman, Principal Analyst at Futurum Research and CEO of Broadsuite Media Group. Daniel is an author, speaker, blogger and educator who works with leading technology brands to help businesses around the world embrace the benefits of digital transformation.
For a growing number of companies, data analytics is now the most important source of customer insights. However, because AI and machine learning technologies are changing rapidly, the role of analytics also continues to expand and evolve.
In the past, organizations primarily harvested descriptive information about prospects, customers and product performance. But now, more companies also extract both predictive and prescriptive intelligence from customer and business data.
This begs several questions:
- How do descriptive, predictive and prescriptive analytics differ?
- Which kind of data analytics makes sense for your organization?
Defining 3 Data Analytics Categories
Let’s start with a quick introduction to the three key types of analytics:
1) Descriptive Analytics
This approach provides information about what has happened in the past. Think of monthly sales reports, web traffic numbers, leads generated from a marketing campaign, customer churn metrics and the like. This data indicates how a project, program or product performed previously. It is the most basic and widely used form of analytics. (Think of it as “analysis” rather than “analytics.”)
2) Predictive Analytics
This method reveals information about what you should expect will happen in the future. Drawing upon more complex machine learning and AI processes and algorithms, predictive analytics helps you anticipate future events. For example, it can help you determine how well a particular product is likely to sell, who is most likely to buy it and what kind of marketing tactics should be effective at driving sales.
3) Prescriptive Analytics
This data considers not only what your company can expect to happen, but also how that outcome will improve if you do x, y, or z. So prescriptive analytics goes a step beyond predictive analytics to recommend specific actions you should take to optimize the results of a particular operational process, product initiative or business strategy.
Actually, we still see a lot of confusion about the definition of predictive versus prescriptive analytics. In some circles, these terms are even used interchangeably.
Regardless, descriptive, predictive and prescriptive analytics all play important roles in today’s organizations. And all involve a fairly sophisticated understanding of statistical methods.
Fortunately, however, complex algorithms aren’t always necessary to find the kind of intelligence we need with data on-hand. Sometimes we just want to know if our business financials are on-track. Or we may want a reality check about whether our social media outreach is getting a reasonable response.
However, many organizations want to move the performance needle. They want to improve efficiencies and optimize outcomes in an informed and systematic way. This is when prescriptive analytics is the ideal choice.
Analytics Example: Marketing Performance
To illustrate the power of prescriptive analytics, let’s look at marketing as an example.
In the past, marketers would draft campaigns and use descriptive analytics to target prospects they assumed would be most receptive.
A marketing team would craft a different promotion for each audience. For instance, with audiences segmented by age, 20-30-year-olds might receive a “younger” marketing message than those ages 45-60. They might even receive offers for different products or services.
This generally leads to better overall campaign performance. And honestly, many companies still market this way. But this type of marketing isn’t as effective or efficient as it could be.
That’s because it’s based on human assumptions, rather than data-based intelligence. This opens the door to bias, blind spots and missteps. Plus, it doesn’t generate insights that help explain why a campaign did or didn’t perform well.
With predictive analytics driven by AI and machine learning, the picture becomes more clear. By applying models that connect the dots between related campaign variables, it’s possible to determine which customer groups will respond best to specific products, offers and marketing messages.
You may also be able to identify which marketing channels and time of day are ideal. But this approach won’t recommend specifically what you should do to improve future results.
How Does Prescriptive Analytics Take Marketing to the Next Level?
This practice combines data modeling, AI and machine learning in a way that requires less human involvement, so organizations can more quickly and confidently match the right buyer, at the right time, with the right content to optimize marketing campaign results.
It also tells salespeople which product and pricing are ideal to recommend in a specific situation. Ultimately, this intelligence makes it possible to maximize overall marketing response and sales volume, as well as pricing and profits.
Of course, the proactive impact of prescriptive analytics reaches far beyond marketing campaigns and sales conversions.
It extends the idea of guided optimization to all sorts of business endeavors, from operations and human capital management to product planning and financial performance.
In addition, by automating predictive analytics, organizations are able to support intelligent, real-time decisions. For example, gasoline and chemical companies do this when changing their pricing throughout the day to maximize overall transactional profits.
Of course, this kind of prescriptive scheme is highly sophisticated. It requires AI to work in the background to perform complex, multi-dimensional calculations on an ongoing basis.
It also requires trust that your analytics tools are working reliably on your behalf to optimize business outcomes.
Decision-makers may not find it easy to relinquish a sense of control over this process.
But active use cases prove that AI is superior to human judgment in optimizing various business functions. Plus, the data created from this kind of process can add reliable feedback into the loop, which in turn, drives continuous improvement.
Which Analytics Approach is Right for You?
To know which type of analytics your company should invest in, you should start with one big question: What do you want to accomplish?
Clearly, prescriptive analytics is powerful stuff. But as I’ve mentioned, it’s not necessary for every company, or even for every customer-focused marketing initiative.
It’s important to understand that prescriptive analytics requires a lot of tweaking. No algorithm is crafted perfectly at the start. It takes time, effort, focus and an iterative approach to work effectively. But if you operate in a competitive marketplace, the effort can mean a huge boost to productivity and profits.
What if You’re Behind on the Prescriptive Analytics Curve?
If all of this seems foreign to you, fortunately, you have time to catch-up. Honestly, it’s still early in the prescriptive analytics game. I think we’re only seeing the tip of the iceberg, in terms of what it can help businesses accomplish.
And especially for smaller companies with limited funds for analytics tools and expertise – don’t worry. My guess is that prescriptive analytics-as-a-service solutions aren’t far behind. This should create a cost-effective pathway to broader adoption.
In the meantime, I recommend that you start paying attention to what others are accomplishing with prescriptive analytics and considering what it could help your organization achieve. Why? Because it’s already proving its worth to others. And I bet it won’t be long until your competitors begin putting it into practice.
Editor’s Note: This post is adapted, with permission, from an article that was published on the Futurum Research blog.
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