Learn how two data-based methodologies – product recommendations and next best action guidance – can help steer your sales team to success.
If predictive selling rests on four pillars – 360 degree learning, comprehensive guidance, intelligent analytics, and ease of use – then comprehensive guidance is based on three main aspects like:
- Who to contact & Why to contact through lead scoring
- When to contact & What to talk about through product recommendations
- How to contact through next best action guidance
Since we’ve already discussed how lead scoring can be extended to direct who you contact and when, we’ll move on to the other two integral parts of this process.
Feel like you’re missing something? No problem you can catch up on this series starting here.
Who, What, When, How, and Why?
At this point, thanks to lead scoring, your team has a prioritized list of contacts. That’s the who and the when. The question now becomes What do we offer them? This is closely followed by Why are we offering this product or service?
Now is the time to employ a product recommendation system. These use sophisticated data analytics to learn from your client’s history; they also “cross-learnâ€, or pull data from similar accounts and analyze it for patterns. The resulting information is extrapolated and used to select products or services that are more likely to appeal to your client. It can also give you reasons as to why each recommendation was made.
The rewards of this approach are obvious. It immediately adds relevancy to your conversations. It gives the client the feeling that you are looking at them as more than a faceless account number. All of this increases the probability of conversion.
However, there is one important caveat to making product recommendations: don’t focus exclusively on new offerings. A mix of established and new products should be used in a recommendation engine. Your clients needs will change over time, so they might change to a new product or repeat purchase for an existing requirement.
Do Product Recommendations Work?
The simple answer is yes, they work very well. As viewers of our recent webinar know, we witnessed the impact of product recommendations with one of our clients, a manufacturer of computer peripherals. They went from struggling to match customers and products to personalized product offers for each customer. This led to an increased share of high-margin peripherals in their overall sales mix as well as to a significant financial gain.
The what, why and when of the product recommendation leads us to another question: How to reach out to the lead? Which channel will be best suited?
The Importance of the How: Next Best Action Recommendations
Finally, let’s talk about how you reach out to your customers. Traditionally, interactions happened pretty much on a person-to-person basis. Each rep developed a sense of when and how to initiate the next interaction with their contact.
Today there are multiple channels that can be used to communicate with a client. For many of us, this only adds one more point to the decision making process. Is email more personal than social media? What about a phone call? A face-to-face meeting? There are lots of options, but the fact is that some clients will respond better if you reach them through a preferred channel. The problem is that identifying which channel can be tricky.
So channel recommendations were born. A channel recommender uses contact history, preference data, and machine learning to determine what channel suits what client at what point in the conversation. The stage of opportunity and the most recent contact channel are also important factors here.
Comprehensive guidance is just that: complete, thorough, and inclusive of every step in the sale process. But what makes all these things actually work? In our next post, we’ll examine how machine learning and decision algorithms power predictive sales. Be sure to read it!
