Pharma Sales Analytics

Empowering pharma commercial teams and field managers to spend time on analysing not crunching complex brick data

In the launch phase for new drugs, measuring your sales activities, effort vis a vis actual sales numbers are a real fact check of your achievements and future success. Knowing your market share, competitor brands and doses, regional sales variations etc. are critical to informing commercial launch strategy. Allot’s brick data analytics solution helped a pharmaceutical company revisit their commercial strategy and activate a flexible operational plan.

About the Client

The client is a global healthcare company that develops medicines, vaccines and consumer healthcare products. Their business spread across the globe with manufacturing and sales of prescription drugs for most therapeutic areas.

Challenge

The client’s pharmaceutical division launched a group of drugs in three therapeutic areas targeting both primary and secondary care in a European country. Previously, launches in this market achieved limited success. There were more than fifty key accounts managers engaged in field force activities. To measure the effectiveness of the launch of the drug and to optimise the field operations, the customer wanted to measure the actual sales performance based on IMS brick data combined with field force activities.

The IMS sales data was in a mix of the different geographical hierarchy of brick, district and region level. It was a challenge to analyse the sales data at a different granular level and mapping to territory level to measure the sales effectiveness.

Transformation

The client was looking for a system that can enhance the IMS sales data and load to a system every month to produce the sales KPIs to be analysed by territory, district, region and country level. Also, the sales performance should be integrated with field force activity KPIs like call volume, frequency etc. to give a 360-degree view of KAM activities vs sales performance.

Some of the Key sales KPIs measured were:

i) Percentage sales vs Market (%MS), Percent sales vs Market last year (%MS LY) and MS Change (∆ MS) by time and geography.

ii) Sales change (%VAR) and super evolution index (SEI) analysis by time by drugs.

iii) Competition analysis like sales trend by top 5 competitions and country level market share of each brand.

iv) Penetration Index (PI) Analysis by Geography across time dimension for a selected market

v) Evolution Index (EI ) Analysis for time dimension for a selected market.

vi) Penetration Index vs Evolution Index analysis by territory, district, region and country.

Additionally, the field force activity (SFE) KPIs were integrated to produce a view of how different SFE KPIs are driving the sales volume. The KPIs analysed were:

1. Sales volume vs call volume by territory, region and country.
2. Sales volume vs call frequency by territory
3. Sales volume vs call rate by territory, region and country

Technology

Allot used a powerful cloud-based data visualisation layer, built using Qlik Technology, that enables users to discover meaningful information and bring data to life with interactive charts, maps and dashboards. The visualisation layer extracted data directly from their CRM and Veeva Systems, removing the need for any manual intervention.

Allot also implemented its field force analytics data model for data collection and analysis. By adding this solution to the cloud, we helped deliver a cost-effective, pay-as-you-go model that can be accessed on both desktops and iPads. The ability to use the solution on an iPad was a key requirement, as the sales managers often need to examine the information while on the go.

Results

Analysis of IMS brick data using the Qlik Sense platform helped the users spend more time on investigating the data in a very user-friendly way instead of organising the data. The sales analytics, along with SFE KPIs helped the management team revisit the plan and make strategic changes. Operationally, it provided the leadership team the ability to make informed tactical changes to field activities, HCP target and field force performance payout for maximum productivity.

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