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The Anatomy of a CRM Suggestion

Wednesday, 23 March 2016
The Anatomy of a CRM Suggestion

By Derek Choy

With the average rep juggling 80-100 physicians in one territory, multiple communication channels and a pharma market that’s changing every day, it’s not surprising that CRM suggestions have emerged as one of the most impressive tools in today’s pharma marketing arsenal. Just a few lines of text have the power to eliminate hours of work for your reps, freeing them up from analyst tasks to focus on building relationships with HCPs—the job they were meant to do. In this post, we’re breaking down our CRM suggestions piece by piece, diving into the how and why behind pharma’s best new tech.

Each section of the suggestion is numbered in green in the screenshot below. Look to the corresponding numbered paragraphs beneath the image for a detailed explanation of each piece.

Please note: The screenshots in this post show how Aktana’s suggestions and insights appear within Veeva’s CRM Suggestions interface.

1. Urgency

Due to a swift change in market conditions or simply because a rep is falling off-pace, it may be necessary to increase the urgency of some actions over others. What constitutes “urgent” is entirely configurable to fit each brand’s unique strategy. You can even set specific dynamic conditions to trigger an urgent label (For instance: If an HCP starts writing scripts for a competitor’s drug, then the suggestion automatically becomes urgent) to encourage your reps to respond to important market developments as they arise. Regardless of whether or not the urgent designation appears in the suggestion queue (some customers choose not to use it at all), suggestions are always ordered by priority. With the goal of relieving the overwhelmed rep, only a small number (7-10 on average) of suggestions—the most critical—appear at one time.

2. Pacing

Overlapping territories. Contractually shared targets. Multichannel initiatives. Striking the right balance between too much contact and not enough is easier said than done—especially as the number of communication channels continues to grow. Aktana constantly reviews both past and future activities for the rep and any counterparts with shared sales goals to ensure that no redundant suggestions are issued.

3. Reasons

Reasons help reps evaluate suggestions within the context of their own territory-level expertise to determine whether or not they make sense to execute. They specifically relate to the suggestion made and provide the pre-synthesized information reps need to decide what to do with a suggestion.

How does Aktana know what information is most useful to the rep? Most decision support engines begin with machine learning, blindly processing mountains of data to identify potential correlations. Unfortunately, this approach tends to generate too many coincidental correlations that aren’t meaningful to the rep—adding to the chaos instead of the clarity. Conversely, Aktana begins by interviewing your top-performing reps (finding out what data they analyze and how it affects the decisions they make) and the brand (codifying base strategies, dynamic market responses and priorities). Machine learning takes the lead after the initial strategy has been set, correlating actions with results for ongoing strategy optimization. The end result? Suggestions that feel right to reps and successfully execute against each brand’s unique strategy.

4. Location

Location is one of several context factors that Aktana uses to produce suggestions that are both on-strategy and pragmatic. Will a rep follow a suggestion that requires driving three hours off-course to visit one HCP? Probably not. So, how does Aktana know whether a suggested HCP “makes sense” to visit? If reps schedule their visit appointments in the CRM calendar, Aktana will connect appointment times with physician addresses and triangulate a proposed route. However, if a rep doesn’t schedule his/her appointments in the CRM, Aktana will learn routes over time, creating a projected route based on habitual travel patterns throughout a rep’s territory.

5. Availability

Availability is another context factor that prevents reps from receiving suggestions that either don’t feel right or have a low probability of executional success. With HCP time and attention at a premium, the rep who knows when Dr. A is historically available or Dr. B is most likely to read and reply to an email has a significant tactical advantage. With every visit placed, phone call made or email sent, Aktana pinpoints when each HCP is most responsive and tailors its suggestions accordingly.

6. Feedback

It’s critical for reps to use their local territory knowledge to tailor future suggestions through ongoing feedback. This helps suggestions become smarter for each rep’s specific context and for the sales force overall. While rep feedback is extremely important, it only represents a small portion of Aktana’s connected learning platform. By correlating rep actions with results, Aktana’s decision support engine constantly evaluates brand strategies, proposing strategy tweaks to continually improve your sales approach.

Insights

Whether or not a suggestion is made, reps can access an overview of the most relevant data points for a given HCP—such as competitor activity, sales trends and engagement level—all in one place.

A prioritized list of insights for HCP Warren Galeos as shown in Veeva’s Timeline View.

Insights are the concise synthesis of the most important and pertinent data available for each HCP. They streamline pre-call planning by shifting analyst tasks off the rep’s plate, and help all reps be more effective.

The Bigger Picture

Like an iceberg, the visible part of a CRM suggestion is only a small fraction of a much larger, complex entity. The most impressive aspect is what you don’t see—the incredible amount of data science working behind-the-scenes to make sure suggestions are smart, strategic and likely to be adopted by your reps. If you’d like to take a closer look, please contact us for a demo.

This blog post was originally published on Aktana's website.

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