Wouldn’t it be nice to know which contact lists are worth acquiring before you buy them? What if you accelerate your sales cycle by enabling your salespeople to work more efficiently? That’s what predictive marketing can do for your organization.
It’s not a crystal ball flying into the middle of the room swirling in purple smoke. There is nothing magical about it. Predictive marketing is the strategic use of existing customer data sets to identify patterns and anticipate future customer behaviors, sales trends, and marketing outcomes. This makes it a perfect fit for a modern marketing targeting strategy. In fact, organizations that leverage predictive marketing are more likely to attract the audience they want.
Collecting customer data to improve future marketing efforts is not a new practice. However, modern strategies use machine learning and artificial intelligence algorithms. This makes it possible to process volumes of information that were previously unimaginable. Today, marketers can leverage aggregated databases of customer profiles to build predictive models, enrich research leads, and analyze the performance of different programs.
One example that may speak to you more, concerning e-commerce sites. When you put a product in your Amazon shopping cart, a predictive model analyzes your purchase history against anonymous records of shoppers with similar preferences and suggests another product that might interest you. With a slight modification, this tactic can also be extremely useful in a B2B context.
How does predictive technology work?
Predictive technologies can identify contact lists that are worth buying. To do this, they will compare the contacts on these lists with the customer data your organization has previously collected. Similarly, analytics tools can expand your organization’s existing leads with data enrichment services. Together, these solutions empower your salespeople to have more productive conversations with prospects, reducing the time it takes to develop the relationship.
Uniquely, predictive models can benefit every stage of the buying chain. As a result, marketers will develop an omnichannel buyer’s journey with the confidence that each step will be consistent. Whether a prospect interacts with your organization through a landing page, newsletter, white paper, chatbot, or phone call, predictive solutions drive engagement through fully personalized interactions.
Predictive marketing can take many forms and is not limited to inbound lead generation or cold calling tactics. In addition, different analytics models can increase the total spending of an existing customer.
Why is predictive marketing important in today’s market?
If business buyers and consumers have one thing in common, it’s the sheer number of options available to them in the marketplace. Predictive marketing is the most attractive option for gaining a foothold in the market. Indeed, the use of predictive data on all your channels will give more possibilities to deliver a personalized message to an interested audience. In addition, predictive technologies integrate with email, social, web, and phone channels to deploy strategic messages at an optimal pace.
Increasingly, predictive modeling is becoming a critical component of marketing automation. For example, adjusting algorithms in real-time provides up-to-date insights for ongoing marketing campaigns. Specifically, predictive models help improve lead generation and sales. In addition, predictive tools can help determine which audiences need special content – such as an e-book or white paper – to achieve goals such as brand awareness or audience education.
Predictive Marketing Best Practices
Every strategy is unique. Still, there are a few fundamentals among successful implementations of predictive technology.
- Understand how predictive marketing supports your goals. Predictive models can benefit organizations in any industry. However, each strategy must be defined with unique goals. SalesForce’s “State of Sales” report found that sales teams with clear goals are 2.4 times more likely to use predictive technology. If an organization’s goal is quantifiable, chances are the company can benefit from a predictive model.
- Evaluate your model. Machine learning algorithms cannot read the future; they are fallible. The models your organization uses should be reviewed and updated by an expert on a regular basis. Likewise, model results must be interpreted by an experienced player.
- Give your strategy time. Predictive modeling recommendations require a period of implementation before they reach their full potential to maximize ROI. For example, a Salesforce study found that the rate of orders influenced by predictive modeling recommendations increased from 11.47% to 34.71% on average over 36 months.
- Don’t wait to implement your strategy. A potential three-year ramp-up is not the only reason you should consider implementing a predictive strategy sooner rather than later.
- Integrate your strategy with sales. Predictive marketing conversations should include stakeholders from both marketing and sales departments. Strategic models can drive lead generation, cross-selling, and up-sell goals. 70% of companies with predictive strategies were rated outstanding in customer engagement, and 66% strong in selling to repeat customers.
Data-driven marketing technologies can bring amazing benefits to marketing and sales teams of any size. Using sophisticated data sets and machine learning algorithms, predictive models identify prospects, predict audience preferences, support automation goals, and drive customer engagement.
You don’t need a crystal ball to predict the future! Use your predictive modeling tools to leverage marketing data and drive sales.
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