How Predictive Analytics Can Help Your Business
Over the past couple years, predictive analytics has taken on a life of its own. For those wondering why it is only now making waves in the digital marketing world, there are three main reasons:
- Accessible data
- Improved computer power
- Simpler software
Now, even small businesses have an opportunity to use predictive analytics for the benefit of their companies. Even so, not everyone is convinced the investment is worth it. To gain more clarity, we’ve compiled a list of ways predictive analytics is being used today. We’ll discuss machine learning, artificial intelligence, and why you should include predictive analytics in your strategy in 2020!
Applications for Predictive Analytics
1. Content Personalization
If you want your company to grow in the marketing ecosystem, content needs to be tailored to the individual consuming it. There’s no use creating content that wasn’t crafted with your target market in mind. With all content, it needs to be relevant and reach the right people at the right times.
Several studies have shown the effectiveness of predictive analytics when it comes to highlighting high-value customers. Once you know the customers with the highest chance of converting, you can think about marketing the right offer to get them interested in your business. With predictive analytics, the quality of your data is pivotal. By using customer behavior historical data, creating a personalized message is easier than ever.
In this area, we’ve seen some great predictive analytic models and one of the most popular is affinity analysis. You may already be familiar with churn analysis or response modelling. Essentially, all three will inform you of the most effective marketing channels. For example, you might want to know if you should keep print and digital subscriptions separated or combine them. We’ve seen businesses use this type of analysis to decide whether content demands a subscription fee or whether it should be given through another pricing structure.
2. Customer Behavior (Predictive Modeling)
If you need an example of companies who predict customer preferences and behavior, look no further than eBay and Amazon. The technology running these systems is no longer reserved for only the largest budgets. While creating a large catalog of predictive models like Amazon takes time, there’s no reason why you can’t adopt some simpler model types.
AgilOne uses predictive modeling in three main categories:
- Predictions (Propensity Models) — Perfect for predictions on consumer behavior, examples of this model type include: propensity to convert, predictive lifetime value, propensity to unsubscribe, likelihood of engagement, and propensity to churn.
- Segments (Cluster Models) — Here, we’re looking for customer segmentation. Through segmentation, we can separate our target market based on demographics, average order size, and various other variables. Popular cluster models include product-based clustering, brand-based clustering, and behavioral clustering.
- Recommendations (Collaborative Filtering) — Just as the name suggests, this type of predictive analytics is used for recommending services, advertisements, and products to consumers. This is based on all sorts of variables, one of the most common options being previous buying behavior. Both Netflix and Amazon use this technique with cross-selling, up-selling, and next-selling.
As we’ve already seen, all three of these models have the potential to be successful. There are examples all around us that we may not even consciously be aware of. Every time we open Netflix or Amazon, predictive analystics is the power behind the recommendations we are given about what to buy or watch. To imitate results that look anything like these companies depends entirely upon the data that is fed into the system. As long as the data is strong, the results on the other side will be of great value for you.
3. Product Development
As a business, you need to bring products to market that will generate interest, and more importantly, sales. In the past, we’d have to look back at data and guess what products would sell well in the current climate. We can do this with far greater accuracy thanks to predictive analytics. While 100% accuracy is impossible, we can clearly make more informed decisions using this marketing tactic.
By using all of your data on consumers and spending habits, you will be able to decide on future products and services based on insights rather than conjecture. With data visualization, physical stores can learn the type of people living in the area, what they tend to buy, income, age, and a whole host of other data points. This process makes pinpointing the type of products that might sell infinitely simpler.
4. Lead Qualifying
Marketers have found that predictive analytics has incredible value when it comes to lead qualifying. In particular, it shines in three categories:
- Identification Models — Businesses can look at existing customers and use this as a foundation to highlight and acquire new prospects.
- Predictive Scoring — Involves listing leads, accounts, and prospects according to priority. Who is most likely to take action and become a customer of the company?
- Automated Segmentation — Leads are segmented to determine the order of priority for personalized messaging.
As we can see, these three forms of B2B marketing aid the sales team and have the potential to help the business become more efficient in converting leads. Rather than wasting time with the wrong leads and allowing stronger leads to be lost, they’re all qualified and prioritized from the very beginning. This increases the sales team’s awareness of which leads are most valuable, providing them with information they can use to close more deals.
The market for small businesses only continues to grow. Compared to even a few years ago, startups and small ventures now have access to more predictive analytics tools. It is important to note, however, that having a high number of sales already in place is critical to building and training a predictive model.
If you work in marketing for a small business, you know that millions of clicks and impressions is much easier than getting actual sales. To make the most of predictive analytics tools, we recommend taking advantage of other marketing strategies first in order to boost sales. Then you can leverage that data required to compound your success.
5. Marketing Guidance
The final admission we have for using predictive analytics to leverage your business is listening to marketing guidance. By looking at social media data, using behavior scoring with customer data, and more, predictive analytics will help businesses to decide which channels are best for a campaign. Is social media the best route? Would it be better to run the campaign through mobile? Predictive analytics can deliver these answers.
Some businesses have found results with sentiment analysis and text analysis. By integrating this with social media data, you can get even more consumer insights. This helps shape product creation and future marketing campaigns.
See Related: Top 10 Predictive Analytics and Lead Scoring Tools
The Rise of Machine Learning and Data
In the past, we’ve seen marketers and businesses ask why now is such an important time for predictive analytics. After surviving for so long without this technology, why is now the time for this type of in-depth analysis? For one thing, most businesses now function primarily online. Even brands who have a physical store and a long history of brick-and-mortar locations still accumulate copious amounts of data online.
Businesses have a presence with:
- Customer relationships
- Finance
- Sales
- Marketing
- Recruitment
Whether it’s a full-blown online store or even just advertising job vacancies, every company is online in some capacity. More than ever before, this data is accessible and usable.
If we were to start a business today, we would have the following pieces of data within a matter of days:
- Impressions
- Time on site
- Organic search traffic
- Customer lifetime value
- PPC ad performance
Within a few weeks, we would have more marketing data than companies in the past would aggregate in a decade. Millennials are infiltrating the working world, and for them, they don’t know anything different than this data-laden environment. For those who have been working in marketing for longer, it’s a different story. Generational differences aside, as long as enough data is going into the system, businesses of all sizes can generate models and use predictive analytics to benefit their business in the five ways we’ve seen.
Fortunately, we don’t have to deal with predictive marketing alone because there are a number of platform developers offering valuable tools. Once integrated into our systems, they collect the data and generate the insights on our behalf. If you make one change this year, we highly recommend boosting your business with predictive analytics!
See Related: Why Predictive Advertising Can Improve ROI in 2020
