What does it take to achieve the same impact as field reps with digital rep equivalents?

16 Mar 2022
What does it take to achieve the same impact as field reps with digital rep equivalents?

As healthcare professionals (HCPs) increasingly adopt digital channels, life sciences brand managers have access to data that's growing at an exponential rate. Brand managers can use this data to identify the type of content that specific HCPs prefer and the channels they prefer to consume this content. They can then estimate the impact of various marketing channels and tactics on prescriptions and sales. They can also forecast the impact of future sets of tactics. These insights help brand managers build omnichannel call plans with personalized journeys which engage HCPs better and lead to the desired prescription behaviour.  

As my colleagues shared in previous blogs, life sciences organizations need to adapt their Go-to-Market (GTM) model for a digital-first world and innovate at the other end (the commercial end) of the value chain. Digital Rep Equivalence is one such approach that delivers the same impact as a field rep, at a fraction of the cost, through a combination of well-orchestrated digital touchpoints. But what does it take to make this vision a reality? It comes to essentially 3 factors – (i) Access to data from millions of digital touchpoints to create robust HCP profiles, (ii) Effective statistical modelling to score channel impact, and (iii) Personalized omnichannel call planning to engage HCPs. 

Here's how you too can achieve the same (or better) impact from a digital rep equivalent as that from a field rep, at a fraction of the cost – 

(i) Use data from millions of digital touchpoints to create robust HCP profiles 

Big data sets like closed-loop marketing activity data, sales data, anonymized patient-level data and demographics information offers deep insights and recommendations on HCP behavior. Use predictive scoring approaches, leveraging machine learning techniques to predict key HCP behavioural characteristics, like their content and channel preferences and their probability to prescribe a certain type of treatment. 

Then apply advanced scoring models considering multiple factors and narrow it to a single score like Digital Affinity - essentially how likely would an HCP respond positively to digital and virtual promotions. Such a score helps you differentiate digitally-savvy HCPs from those who prefer more in-person engagements. 

Here's an illustrative equation to calculate a Digital Affinity Score – 

The channel scores are predicted engagement scores and are calculated at an individual channel level. w1, w2, … wn are weights assigned to these channels based on the importance of these channels to the overall conversion. 

You can use these scoring models to create derived variables, which are then used to define multidimensional HCP personas for smarter targeting and prioritization of your brand's sales and marketing efforts. These multidimensional personas also help you personalize the sales and marketing strategy, resulting in better customer experience and a higher engagement. 

(ii) Statistically model and score channel impact 

Marketing and sales data enables brand managers to answer key questions like - What is the impactable sales distribution across channels and the carryover impact. Statistical modelling (regression analysis) assesses the historical impact of each channel on brand sales. You can use the output of such models to estimate relative impact of each marketing channel in reference to field sales reps, thus deriving a Relative Impact Score for each channel as well as a combination of many channels. 

A modeling equation, using sales (Rx) and channel interaction data available at HCP level looks something like this – 

TRxpost = α.TRxpre + β1.fn(F2F callspost) + β2.fn(channel 1post ) + ⋯ + βn.fn(channel npost )

Where, TRx is the total number of prescriptions, β is the relative impact of each channel towards new sales ( TRxpost ), and α is the carryover effect from past sales ( TRxpre ). With such modelling, you can derive Channel Impact Scores for each of your segments or microsegments.

Statistical models use data from benchmarking studies and customer experience surveys to predict changes in customer behaviour and factor them in to the models to adjust channel impact scores. For example, in-person meeting and events will have a reduced impact score now compared to pre-Covid-19 times due to a change in HCP behaviour during the pandemic. Similarly, the impact of digital channels and virtual meetings will continue to be higher in the post-pandemic era. 

(iii) Personalize omnichannel call planning to engage HCPs more effectively 

Based on multidimensional HCP personas and relative impact scores of the available channel mix, brand managers can build omnichannel call plans which rightly mimic the field rep experience. These call plans with personalized journeys fuelled with simple, relevant, trustworthy content better engages HCPs and drives desired prescription behavior. 

Personalize omnichannel call planning to engage HCPs more effectively

Omnichannel call plans are optimized with business constraints or spend targets to arrive at a channel mix best suited for a specific sales target. You can then use what-if simulations to measure the performance of multiple scenarios and choose the best one. 

Conclusion

Life sciences brand managers feel the need to disrupt their Go-to-Market (GTM) models for an increasingly digital-first world. The digital rep equivalence approach is a key enabler on this journey. All you need is access to a high volume of digital interaction data to create robust HCP profiles, effective statistical modeling to score channel impact, and some personalized call planning to engage HCPs more effectively. The benefit is significant – same impact as a field rep at a fraction of a cost. Now that's what enables truly #FutureReadyHealthcare