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Predictive analytics services: 5 things to keep in mind

February 2, 2021

Predictive analytics houses the power to reveal valuable customer insight, like what content consumers are likely to read and when they’re most likely to buy. Predictive analytics is the use of statistical algorithms, data and machine learning tools to identify, based on historical data, the likelihood of future outcomes. Far more organizations have turned to predictive analytics to gain a competitive advantage and increase their bottom line.

Historical data is typically used to build a mathematical model that captures essential trends. This predictive model is then applied on present data to suggest actions to take for optimal outcomes or in other words, to predict what will happen next. Predictive analytics has received much attention in the past few years thanks to advances in supporting technology, particularly in the areas of machine learning and big data. Below are the 5 things we tend to overlook when it comes to predictive analytics.

  1. Many believe that predictive analytics is far too challenging an approach to implement in the growing stages of a company. However, with the variety of tools available today, rolling out a predictive plan is very doable. People assume that it’s this unattainable ideal or that it’s too advanced for them to get started, which isn’t true. Even for smaller teams, there are enough tools out there today that make predictive analytics really accessible. It’s only a matter of aligning the organization with the goals and making that change.
  2. Certain predictive analytics models rely too heavily on narrow, single-source, third-party buying behavior data sources that discriminate where consumers go to do their research. They are often limited by a very small arrangement of behavior topics which could skew the results from predictive models and segments. Some predictive models also only take a snapshot of behavior within a minimal timeframe. It’s crucial to spot trends over time and assess what’s just regular activity versus behavior related to a buying journey.
  3. “Predictive analytics” is being tossed around far too freely today. There’s predictive analytics for social media, marketing, banking, health, entertainment and more. However, it has to be business specific for a predictive analytics engine to work for you. The fact is that unless the software you choose to make use of is taking all the factors of your particular business into account, it will not be of any value to you. A generic analytics tool, no matter how savvy it may seem, is not what you are looking for. Find a software vendor that understands your business, and has a tailored solution for your specific needs. When you find this software, then ask for an analytic assessment. If their engine is as smart and perfect for your supply chain as they make it seem, hold them accountable by having them quantize the benefits it'll provide you in the future. After all, they should be experts at predicting ballpark numbers by now.
  4. Determining which data points they use and how providers build their predictive models is a crucial starting point. Ensure the vendor can factor the place where your customers go to do their research into their modeling. Check for evidence of results from customers to ensure they can deliver on the promise. Find out if they genuinely understand B2B buying behaviors and customer journeys. For instance, have they any history or experience with B2B marketing? Also, how has the experience been used to construct better predictive models to improve upon what they are currently doing today?
  5. When predicting future results by extrapolating past performance, one must always proceed with caution. Past performance does not guarantee future results. So basing your decisions on historical data without taking into account the circumstances and context will harm you more than help you. Focusing exclusively on the “what” aspect of past performance only gives you information. However, the “why” aspect of say, a sales report is equally essential. A bad sales run could also be due to unavailable on-hand inventory, not just lack of demand. It's very easy to draw incorrect, seemingly obvious conclusions unless the context is taken into account. Focusing on the “why” will give you an idea about the factors that resulted in those numbers. It becomes easy to make incorrect decisions about the future when the context is not factored in. Predictive Analytics differs from traditional analytics. You must identify and capture causal factors to get started with Predictive Analytics. Some examples of causal factors that would impact Sales are New Product Launch, Investment in Marketing, Holiday spending, Seasonality, Geography, and Opening of a new store. Since one can't capture all causal factors, every prediction won't turn out accurate.

As long as you avoid relying too much on single-source and very narrow third-party buying behavior data sources, predictive analytics can deliver promising results. Look beyond and around the historical facts and figures. Historical data lays out a good picture of what has happened, but understand that extrapolating comes with its risks. Choose a vendor who understands where your users do their product research and makes use of correct data points, and you should be well on your way to predictive success.

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