How Predictive Content Recommendations Can Help You Close More Deals
In B2B today, there is a lot of talk about predictive analytics and how they can transform your business. Predictive analytics refers to the branch of advanced analytics that incorporates data mining, machine learning, modeling, and artificial intelligence (ai) to make predictions about unknown future events. Predictive analytics leverage current and historical data from a wide range of enterprise sources to make predictions about the future.
Rolf Olsen, the chief data officer at Mindshare put it nicely when he explained that “predictive marketing is really about how you take the backward-looking components and make them forward-looking to—for lack of a better word—predict outcomes.”
There is a wide range of benefits which can be derived from predictive analytics including better sales forecasting, pipeline building, lead nurturing, and ideal customer identification. Yet, there is one area which has been somewhat overlooked and that is predictive content recommendations – and specifically how they can help your reps to close more deals.
Predictive content analytics can help you see and understand everything about your content.
1. Identify Perfectly Tailored Insights for Each Sales Situation
Customer’s today expect personalized interactions through each stage of the sales funnel. Unless your materials resonate with the customer’s specific situation, you will not be able to get the deal to move forward.
Some companies have tried to ensure that their reps are always on point by leveraging a prescribed or guided selling methodology. This methodology often involves the development of the dreaded buyer personas. These personas are fictional characters developed to represent the different customer types of a given company. A would-be expert in marketing or sales will then come up with a simplified set of predictions about how each persona will act throughout each phase of the sales cycle. Content is then mapped to each persona and stage. This content is then prescribed to sales reps to use when these situations materialize.
Prescribed selling is nice in theory, but, in reality, it is an absolute waste of time. B2B reps today are faced with new and unique situations every day. There is now an average of 5.4 stakeholders involved in any one deal. Furthermore, consensus-based decision-making is now the norm. Reps must present materials that resonate with each stakeholder. There are other variables too which reps must consider namely:
- Product type
- Legal and contractual requirements
- Key stakeholders
- Use case
- Deal stage
- Financial terms
Now think about all the permutations and combinations that make up each selling situation. There are literally millions of potential situations a rep will face. The notion that an imaginary buyer persona will help them win over a prospect is outlandish.
Reps need all the help they can get, but the answer does not lie in oversimplifying the complexities of B2B sales. Instead, reps need a predictive content analytics solution that helps them meet the complex challenge they are presented with today. Predictive content recommendations can help reps to deliver the perfectly tailored presentation to prospects. Using predictive content analytics, you can leverage the entire corpus of corporate knowledge within the company and present reps with content recommendations that take into consideration a range of factors like usage rate, association with winning deals, date, deal size, and industry – and, in doing so, provide a better content recommendation than a human ever could.
2. Predictive Content Analytics Keep Increasing in Effectiveness
The great thing about the machine learning technology which predictive content analytics are based on is that – the more it is used, the more effective the recommendations become. Predictive content analytics solutions incorporate a range of signals, inputs, and sources that ensure content recommendations become more effective over time.
- Content usage: The more regularly a piece of content is used, the more likely it is to be relevant. For example, if there is a particular ROI slide or case study slide that is frequently embedded in presentations and collateral, that is an explicit vote by users of the the efficacy of that slide. Other usage indicators, include other actions such as downloads, exports, copies, or views
- Content freshness: Freshness is another key indicator of how relevant a particular piece of content is likely to be. For example, if there is two presentations targeted at companies in the same industry, the one that is freshest is most likely to be the one that is most relevant.
3. Predictive Content Analytics Provide Marketing With Usage Insights
Marketing teams are spending a huge amount on content, unfortunately however, at many companies, there is little or no visibility into how this content is used by sales. Unless marketing knows which materials are being used by sales, then they have no way of knowing whether their materials are resonating or not. Predictive content analytics not only predict and match content to each sales situation, they also provide marketing and sales leaders with come much-needed visibility into how content is being leveraged in the field.
For content analytics to really work, they must index content, interactions, and data points right across the enterprise. It is not enough to provide visibility into how content is used within the walled garden of a particular system or platform. Instead, analytics must index structured data like the activity that takes place within the CRM and also unstructured data like email, wikis, pitch books, phone calls, meeting notes, cloud drives, and SharePoint sites.
With disparate enterprise functions still operating and storing data in silos, valuable tribal knowledge goes to waste. So instead of using the intelligence in this example to inform strategy, it gets locked away and lost to the organization. Predictive content analytics can help you to unlock these invaluable insights.