Artificial Intelligence is not a new secret weapon that marketers just discovered in their quest for World Domination.
The rise of machine learning is a direct extension of human development and the rapid improvement in computing capabilities.
The term “Artificial Intelligence” was actually coined before most adults working today were born., At a conference in Hanover, NH in 1956. At Dartmouth College.
Arguably, the most important element of AI being developed by companies who use AI in their marketing is Predictive analytics.
Predictive analytics is a branch of advanced data analytics that refers to the process of using data mining, statistics, and AI models for data analysis to make predictions about future outcomes. This analysis is done with highly-specialized software running on the fastest machines with computing power allowing the processing of vast amounts of data accurately and in real time.
In business, predictive models utilize historical and transactional data to identify patterns that make it possible to recognize risks, capture opportunities, and enhance decision-making.
The size of the data set is the reason that machine learning is required to facilitate running the statistical model. In 2020:
- Over 2.5 quintillion bytes of data are created every single day
- By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on earth.
Think of it this way: Having access to reliable data for planning, designing and deploying a marketing campaign is like having a superpower that almost guarantees success.
The steps in developing a predictive analytics process are as follow:
- Define Objectives: Determine which business questions you want the data to answer, like “Who is interested in purchasing a solution from my product menu in the next 12 months?”
- Data collection: Have a plan for which data you need, how you plan to collect it, and the best ways to organize it.
- Data analysis: Inspect data for useful information and form conclusions about your customers.
- Statistical : Determine success rate of objective outcomes.
- Modeling: Create predictions about your customer’s future behavior.
- Deployment: Utilize the data to inform marketing strategies and implement tactics.
- Model monitoring: Track and report on the effectiveness of predictive data driven campaigns.
How Does Predictive Analytics Help Your Business?
Once you have created the infrastructure that enables the effective development of a viable statistical model, you can use the strategy to improve your business in at least 8 significant ways:
1) Create More Detailed Lead Scoring
You can begin by Ranking leads based on where they are in the funnel. Content Insight will provide this information. Lead scoring allows marketing and sales teams to integrate in a more meaningful way, since every lead is different.
With prescriptive analytics, every lead will be scored based on their readiness to purchase. This helps to anticipate the next step in marketing or selling to a prospective lead based on predictions about their future buying habits.
2) Lead Segmentation to Improve Prospect Nurturing
Lead nurturing, a consideration through the entire buyer’s journey, requires planning and strategizing.
Using both demographic and behavioral data, predictive analytics helps businesses group leads by interest.
Enabling the creation of targeted lead nurturing campaigns, tailored specifically to move the prospect to the next step in the sales funnel.
3) Target Content Creation and Distribution
Which type of content will work better for certain leads is a question that can be answered with predictive analytics. Once you know not only which type of content resonates with a specific audience, but what channel to best reach them on, you can customize content creation and distribution.
When leads receive higher-quality communication from your company, there is an increased probability of conversion at any point in the funnel.
4) Predict Lifetime Customer Value
LCV is the Ultimate Measure of your Marketing ROI.
Lifetime Customer Value is how much money a customer is worth to you for the duration of your relationship. Developing raving fans for your solution is the end game for your effort.
Using predictive analytics, you can analyze historical data for each customer and use it to forecast the future lifespan of your relationship with them as well as how much cash that relationship is likely to bring in.
These estimates can help you budget for customer acquisition, giving you a more reliable ROI.
5) Churn Rate Prediction & Reduction
Churn rate is the rate of customer attrition, which is the percentage of customers who stop a subscription payment, shop your product to your competition within a certain period or only make a single purchase.
In order to grow, a business must have a higher retention rate than churn rate. WithUsing predictive analytics, you can identify the warning signs that alert you to the loss of a customer and allow you time to follow-up, nurture or re-engage before it’s too late.
6) Upsell and Cross-Sell Opportunities
Using available data about customer buying behavior, businesses can upsell, cross-sell or combine both to increase profit.
For example, if you know that X% of customers who buy product A from you come back to buy product B within six months, you can then start to market product B to customers shortly after they buy product A to speed up that process and capture those who might not have otherwise considered purchasing product B.
7) Understanding Product Market Alignment
Equipped with historical purchase behavior and lead data, businesses can better understand exactly what their customers’ need and want.
This may translate to developing products in the future to meet those needs or improve on existing products that aren’t meeting their sales forecast.
8) Optimizing Cross-Channel Marketing Campaigns
With predictive analytics, businesses can better plan, develop, strategize and implement future marketing campaigns.
The more you know up front, the more successful your targeting and messaging will be.
By applying predictive analytics, risks can be significantly reduced because decisions will be made based on data, not merely unproven assumptions that rely on instincts and some educated guesses.
Many successful e-commerce companies have already adopted predictive analytics in their marketing efforts.
It should be no surprise that Amazon is the king of using predictive data to target and remarket to customers with great success. But you are seeing more of the tactic in smaller businesses now in 2020. Every time you shop online you see examples of the strategy. Some are much more sophisticated than others.
Developing predictive analytics sounds like a massive undertaking for smaller companies to deploy the new technology. And unless you have both a robust IT department well integrated with the marketing department, you’re going to struggle to use predictive analytics in your marketing.
The success of predictive analytics doesn’t depend on any single department. Instead, you need a company-wide effort with participation from all departments including marketing, sales, operations, finance, support and services. Along with the back-office processes like logistics, inventory management and distribution.
Other important factors include participation of C-Suite management, complete alignment with business strategy, quantifiable objectives and a work culture where data-driven decision making can succeed.