AI and data analytics within a pharmaceuticals company
Customers create data with every interaction they make. This provides the company insight on the decisions they make during the interaction. The interactions are captured and stored in databases and form the Customer behaviour. With digital transformation, a company website captures much more information based on the products the customer interacted with, pages they visited, articles they read, length of time they shopped and if they made a purchase. The purchase itself produces more information such as elapsed time before the customer made a purchase, if the customer used a discount code or not, and where they referred to or clicked on an ad.
Previously we could only look back at prior customer behaviour and make informed decisions. Today, with AI enabled technologies, we can now use past Customer behaviour to predict future behaviour and address some of the challenges that Sales and Marketing Departments face such as:
- Inaccurate sales forecasting
- Improve quality of prospects
- Customer retention
- Personalization of offers and communication
- Audience selection
The next sections of the article explore the different scenarios where AI can be applied in the Sales and Marketing Departments to address the challenges above for competitive gain.
The Diagram below describes an overview of the steps to utilize AI.
Sales Forecasting
Sales Forecasting is a critical factor in the growth of the business. It has usually been done in a combination of historical sales analysis, pipeline analysis and gut. It is fair to say that most sales forecasts tend to be ineffective or inaccurate.
Pharmaceutical companies can use AI in planning activities specifically for sales forecasting. Historical sales data are fed into the AI model (ARFIMA, Box Cox Regression, Exponential Smoothing, etc.) that generates sales forecasts. The sales forecast can be done monthly or quarterly and at varying levels of granularity.
By using AI Algorithms, Pharma companies can increase the accuracy of their sales forecasts. The predictions provided by AI on Sales Channel Effectivity, Customer Segment Performance, Product Performance and Customer Engagement enables them to set attainable targets/quota, proactively make decisions and identify opportunities to increase/decrease investments.
Customer Acquisition
New Customers are the drivers for an organization’s growth. AI can analyze profiles of customers who buy the company’s product based on demography, interests and purchase behaviour. This allows an organization to identify their very best prospects by running lists on a Look Alike Model.
Lead Scoring Models can then further segment the prospects. AI identifies the customer profiles that have a high probability of conversion and applies them to select the best prospects from the whole population. This is useful for marketers because it helps them optimize campaigns in terms of target size, quantity, spend and messaging.
In closing, AI can have a significant impact for Sales and Marketing Departments in Pharmaceutical companies by unlocking opportunities to gain insight on customer behavior, select the correct audience, send them relevant offers and communication at the right time. Tenthpin Analytics practitioners have deep experience and knowledge on AI enabled technologies and Data Analytics and its practical application to solve current and emerging business challenges in the Life Sciences Industry.
Contact our industry experts at Tenthpin if you would like to learn more.
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