Data is knowledge, and knowledge is power. We’ve all heard the saying that history repeats itself, and the same often holds true for data. Of course there are outliers, but more and more marketers are turning to predictive analytics to find common patterns, factors, attributes, and behaviors that make up their past audience to predict their best future customers.

It’s no surprise that knowing who your customers are, where they come from, and why they come to you is valuable information. Knowing when, where, why, and how to target, segment, and create offers of value to your varying prospect groups with their profile in mind, is what makes marketing – well, marketing.

Predictive analytics takes these principals a step further – by allowing businesses and marketers to fully unlock the power of their data to predict the future behavior of current customers, as well as identify new opportunities for acquisition.

86% of companies that have been carrying out predictive marketing initiatives for at least two years saw “increased return on investment,” according to a study from Forbes Insights. Meanwhile, 63% of businesses that participated in a study by Dun & Bradstreet acknowledged that data has enabled them to identify new opportunities for revenue growth and improve the service provided to their customers.

Predictive analytics starts with high quality data collection

So far, this all sounds great. Gather information and data points, throw it all into a bucket, and see what bubbles to the top.

Except, that’s not how predictive analytics work. Certainly, you can go that route, but most likely, you’ll end up disappointed. Predictive analytics that deliver high response rates and actionable insight start with your data.

If you build a predictive model off of low quality or bad data, it’s not surprising that the model will most likely fail. Good predictive analytics requires good data collection and maintenance from the very start.

Take into account the rapid rise of digital channels, social media, e-commerce, mobile purchasing. There’s a huge volume of data at marketers fingertips, which can be both a benefit and a drawback. The obvious benefit is that it’s easier to create and implement marketing initiatives that can be deployed based on real-time triggers and events, and increase response rates exponentially.

The drawback to this information is keeping a cohesive grip on multi-channel touch points, and attributing all interactions, whether email, direct marketing, event marketing, etc. correctly. It can be incredibly difficult to maintain consistency in data collection and tie together multiple channel interactions, as well as maintain high quality data standards.

No matter what your marketing strategy, it’s important to gather as much high quality and relevant data of possible. Of course, the type of data might vary depending on your industry. For example, healthcare providers might want to collect information on patients who never miss their preventive appointments; while retailers will want to keep an eye on customers who made a second purchase. Some relevant information might include:

Customer Data

  • Full name
  • Address
  • Email address
  • Phone number
  • Relevant demographics

Transaction data

  • Products purchase
  • Location of purchase (whether a brick and mortar store, mobile, web purchase), etc.
  • First purchase date
  • Last purchase date
  • Lifetime purchases

It’s also hard to know, of all of this tremendous amount of data – what matters? That’s where defining your top ideal outcomes comes into play.

Define the outcomes that matter

Once you’ve got your data compiled, the next step is to decide which stage of your marketing you want to look at in terms of predictive behavior. There’s quite a few different applications, and some may matter more to you than others in the long and short term:

Predictive Analytics for Lead Marketing

  1. Lead Generation – Predictive analytic models can help marketers determine a potential customer’s propensity to buy with more accuracy. By determining the high significance characteristics that are shared among top performing high-value customers, marketer’s can analyze those attributes and market to prospects who look the most like their ideal customer.
  2. Lead Scoring – Improved lead scoring involves ranking leads based where they are in the funnel. Prescriptive analytics allows you to score leads based on their likelihood to purchase, which can inform the marketing strategy you use to reach out to them, or sell to a prospective lead based on predictions about their future purchase behaviors. It often helps marketers and salespeople understand when a lead is close to converting and may need additional outreach.
  3. Lead Segmentation – Similar to lead scoring, this use of predictive analytics is designed specifically to move prospects through the sales funnel. By grouping leads by demographic, behavioral, and psychographic data, marketers are better able to create high impact lead nurturing campaigns.

Predictive Analytics for Optimization

  1. Content Targeting – This kind of analytics allows marketers to analyze the types of content that most resonate with certain customer profiles based on demographic or behavioral backgrounds, enabling the distribution of similar content to similar leads, reducing the risk of time and money going to content that won’t have a high impact.
  2. Customer Profiling– Using predictive analytics in combination with high quality data can provide a map of customer journeys and understand where you’re succeeding, where you have opportunity, and what areas may be a waste of your marketing efforts. Having this kind of understanding of your customer base can drive strategy decisions, marketing decisions, product and service decisions, and beyond.
  3. Lifetime Value – This is arguably one of the most important applications for predictive analytics, allowing companies to understand their customer’s lifetime value by providing a historical lifetime value overview of current customers – which, in turn, can be used to predict a new customer’s lifetime value .
  4. Identify Cross-Sell Opportunities – By applying analytics to available transaction data gathered on previous customers, businesses can more effectively predict customers who are more likely to respond to cross-sell or up-sell efforts.
  5. Identify Opportunities for Growth – Another significant purpose that predictive analytics can serve include deep insight into new trends, allowing marketers to rapidly identify and adjust products, services, and campaigns to take advantage of a new market share or prospect pool. By understanding where their highest levels of growth currently and historically stand, businesses can pursue opportunities that have a much higher likelihood of following similar success trajectories.

Knowing what outcomes you want from your data will help drive the next step, which is creating a predictive model based on what information you want to know, and what data you have to feed the model.

Leveraging predictive analytics into effective marketing campaigns using models

The next step in implementing predictive analytics into your marketing efforts involves working with a data team to create a predictive model to accomplish your goals.

The three primary types of predictive models are:

  • Cluster modeling, which identifies significant variables and segments customers by multiple variables at once. This allows for targeting of demographics and personas through behavior clustering, product clustering, and brand clustering.
  • Propensity modeling predicts customer behaviors based on predictive lifetime value, engagement estimates, propensity to convert, buy or churn.
  • Collaborative filtering is useful in upselling and cross-selling by automatically serving recommendations for products, services, and content based on past actions such as buying behavior.

In order for predictive models to be effective in delivering higher response rates, a score must be created that can be used to predict the possibility of future behaviors. These include

  • Predictive scoring, where prospects, leads, and customers are prioritized based on their likelihood to purchase or close
  • Identification models in which prospects are identified as potential high value acquisition leads based on similarities to existing customers
  • Automated segmentation, where leads are segmented for the purpose of serving personalized content.

Once a predictive model has been created, and the prospect or customer universe has been scored; it’s time to deploy campaigns based on the insight that predictive campaigns provide and continue measurement efforts to make adjustments as needed.

Conclusion

Predictive analytics can transform every aspect of your marketing. It provides a cohesive view of your customers, your prospects, your leads, and your marketing efforts’ effectiveness.

By growing your knowledge and insight into the behavior buyer funnel; customer behavior and segments; and prospect lifetime value potential, you’ll be able to make more informed decisions, not only in marketing, but in holistic decision making, from content creation to which products and services are capturing you the largest market share.

Ready to get started?

As challenging as it can be to understand, implement, and measure predictive analytics successfully, there’s good news. Strategic data analytic experts, such as ours here at Altair Data, understand the challenges that businesses face when developing custom analytic models. Our predictive model tools and solutions are here to take the guesswork out of your marketing. Schedule a free consultancy call with one of our experts to learn more about how our predictive analytics can help you identify opportunities for growth.

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