In the first part of our series, we looked at best practices for identifying your brand’s unique requirements and setting up your suggestion platform. Now that your suggestion platform has been configured, how can you ensure your reps will actually adopt the suggestions it generates?
It’s unreasonable to expect reps to heed advice that contradict the instincts they’ve cultivated over an entire career—especially from a newly introduced and unfamiliar tool. But when suggestions don’t “feel right,” that’s exactly what we’re asking them to do. In order for reps to truly engage, suggestions must be clear, concise and accurately reflect the real-world situations reps face every day.
Too much information, too little time: Your reps’ key challenge
Given the increasing number of data sources and priorities that today’s reps must stay on top of, it’s easy to see why suggestions—a tool that automatically processes these mountains of data to highlight only what’s meaningful in the moment—would be so advantageous. But when suggestions aren’t designed correctly, it’s only too easy to exacerbate the very problem they’re meant to solve.
If you are generating multiple threads of suggestions that address different use cases, duplicate priorities or potentially suggest conflicting actions, the rep will still feel disoriented and overwhelmed. And as the use cases for suggestions continue to grow—like adding marketing automation integration and triggers into the mix—the risk only increases.
The Critical Few
In order for suggestions to be truly helpful to your reps, they must be designed to be simple and easy to digest. If we overwhelm or distract reps, we’ll lose them before we get started. Specifically, that means suggestions should satisfy the following conditions:
- They’re synthesized. Suggestions made at any point are aggregated into a single feed. Any duplicated or conflicting suggestions are eliminated.
- They’re prioritized. Limit the number of suggestions the rep can see at one time (we recommend no more than 7-10).
- They provide context. Suggestions should provide all the information a rep would need to make a decision—not just the current trigger.
I’ll use this suggestion to visit Dr. Galeos as an example.
According to the suggestion, Dr. Galeos is due a visit for the following reasons:
- I have remaining details in my call plan, and it’s been awhile since my last visit.
- Dr. Galeos recently attended a congress on safety.
- Labrinone sales have dropped significantly in sales last month.
In this case, not only do we want a single suggestion to be made about Dr Galeos that includes the context from all three data points, but we also want the suggested message to focus on safety (as a follow up to the congress). Finally, the suggestion might be made sooner—and appear higher in a prioritized list—due to the multiple relevant data drivers.
Suggestions That Feel Right
Because reps are only seeing a few suggestions at a time, the suggestions that do appear need to “feel right” to reps. After all, you can only make so many bad suggestions before a rep stops paying attention. So, in addition to properly synthesizing all business priorities and goals, suggestions also need to account for practical circumstances and give reps context.
- If Dr. Galeos had already been seen the required number of times in the call plan, we might not want to make another suggestion to visit him regardless of other data drivers.
- If Dr. Galeos was just visited recently, or a counterpart in a mirrored territory has a visit scheduled in the CRM for tomorrow, we may not want to suggest an immediate visit. Instead, it might make more sense to provide the counterpart with the insights for his/her pre-call planning.
- Similarly, we want to account for a rep’s practical context, like location. If Dr. Galeos is located in the east side of a rep’s territory, but the rep has scheduled calls today for the west, we might want to wait until the rep is likely to be closer to the HCP to make the suggestion. If we knew Dr. Galeos was a no-see HCP and had a preference to digital interactions, we might also suggest an email rather than a visit.
Ultimately, suggestions should play the same role as a golf caddy does for a golfer. Like a caddy, we should offer the best guidance we can based on the latest information, but not without providing the key data points, reasons and insights that the rep needs to make the final call.
Coming up next: Managing your suggestion platform over time
Like individual suggestions themselves, your suggestion platform must be dynamic enough to grow and change alongside your evolving business. In the final part of our series, we’ll dive into best practices for managing and maintaining your suggestion platform over time.