Leading Marketing Excellence with Next-Generation Analytics

Many companies are not as "leading-edge" in terms of analytics as they think.



Achieving marketing excellence is based on making good marketing decisions. Making good marketing decisions involves highly skilled marketers, strong data and, above all, quality analytics. If you have good people, great data, and superior analytics, the customer will be at the center of all your strategies and operations. When you get this right, the customer experience will grow, and when that grows, so does revenue and profitability.

Essentially, it is all about making faster and better decisions that put your customers first, that are valued by your customers, and impact your profit at the end of the day. Yet, many Pharma companies do not seem to be taking advantage of the full potential of marketing analytics to really optimize both the customer experience and their profitability.

There are many ways analytics can be deployed and utilized. Both the mathematical underpinnings and technology are enablers.

Since marketing excellence requires analytics, marketing teams need to create an analytics culture. They should not be going to the IT teams for analytics but should have the tools and knowledge themselves to be able to understand the analytics to be able to make better decisions. This is not as widespread as one would expect or hope. HBR did a recent survey and found that even companies that described themselves as ‘leading-edge’ in analytics were actually not as innovative as they thought; the majority were still using linear approaches, which are by no means ‘leading-edge’.

There are many operational challenges to being innovative in analytics and these are things such as:

1. Data silos:. So much data is in silos in different parts of the organization, and many times the data being collected means reinventing the wheel from some other part of the organization. Not knowing what exists and where are major challenges within Pharma.

2. Dirty data:. Much of the data used is very dirty. Most government and other databases that much aggregated analytics is performed on is dirty data. It is critical that you have cleaned any dirty data before putting it into any analytics or the insights you get will be erroneous and not lead to the winning strategies and tactics you had hoped for. Always understand how dirty your data is and what that means for your results.

3. Functional silos:. Many companies have functional silos and much more cross-functional collaboration is critical, not just between functions like analytics, marketing and IT, but across other functions as well.

4. Legacy systems:. These, no doubt, were expensive to install and are difficult to replace, but if they are not serving the requirements of today’s needs, they should be questioned and suitable replacements considered.

The companies that succeed in marketing excellence today do several things well with their analytics:

1. Start with a specific problem and stay focused: For example, say your market share is flat or in decline, and a competitor is beating you. Use analytics to understand where the competition has the strongest edge, and how and where they are vulnerable. What about pre-launch planning, where you need to ensure that your brand has the optimal messages and channels to accelerate uptake upon launch? Analytics is the perfect tool to examine these kinds of issues.

2. Start with strong data: Data is the foundation of everything. If you don’t use the best data, you will not have an accurate solution. Historical data or analogs are highly unreliable in the dynamic and complex Pharmaceutical market. You also need clean data – see our article Dirty, Dirty Data. We all know the saying: “Garbage in, Garbage out”. Get the cleanest, highest quality data you can afford to put into your marketing analytics.

3. Utilize strong nonlinear approaches: Linear approaches - such as promotional response curves and multivariate statistics - were once all we had, so they had their place. But now that we have access to Artificial Intelligence approaches, such as machine learning, which provide far more granularity in the insights, far stronger information on the synergies between attributes, and far more accurate real-world results, there really is no plausible argument for returning to the old ways anymore.

4. Understand each of your customer’s value propositions, by context, through strong analytics: Data must come from your customers. Without understanding what is driving them, you will not be able to impact your results. The trick, however, is to ensure that you get customer insights but don’t not rely on them as fact. As Malcolm Gladwell wrote in his best-selling book, Blink, consumers rarely know what truly influences them. Always keep in mind the adage attributed to Henry Ford: “If I asked people what they wanted, they would have said faster horses.” That means understanding the customer not through focus groups but through data and math. Simply put, the industry needs to move forward from the simple linear analytics techniques that have been used for decades to nonlinear approaches that incorporate more complex next-generation analytics, which are hypothesis-generating approaches and all based on Artificial Intelligence.

5. Focus on the most valuable opportunities: There will always be many things you can change to impact sales and market share, but do you have the budget for all of them? No, not anymore…if you ever did. Stay focused on the specific changes that provide the fastest results, and use next-generation analytics to help you identify what they are.

6. Create easy-to-understand data: It’s wonderful to have great data, but if it’s difficult to understand and use, it’s worthless. An executive from one of the top five Pharmaceutical companies described how he spent 2 years working on the company’s internal analytics approach but nobody understood it, so were not using it. Instead, the people providing the analytics need to make the results comprehensible to people who do not have PhDs in math so they can understand how to use it. Create a program with a simple interface to allow the least mathematical marketer an easy to understand and use approach that doesn’t require an advanced degree in statistics to understand – even for complex Artificial Intelligence based approaches.

7. Move quickly: You don’t want a process that will take 6 months or longer to provide answers. Remember: time is of the essence. You want the data to be current so that you can make real-time changes to affect sales and profit within the next quarter, not the next year.

Conclusion

In leading companies, marketing analytics allows the marketing team to put the customer at the center of business strategy and operational execution. When the customer experience improves across the organization, so does the bottom line and meaningful business results for the C-Suite.

Many Pharma marketers are not in the game when it comes to analytics. Very few are implementing leading-edge analytics that involve Artificial Intelligence, especially machine learning and deep learning, even though these all exist already for Pharma marketers, are simple to use and will produce strong results.

Remember, it’s all about understanding and giving value to your customers. Implementing next-generation, Artificial Intelligence-based analytics helps you achieve this faster and more easily than ever before.


For more information on Pharmaceutical marketing analytics and easy-to-implement and use, Artificial Intelligence based approaches for Pharma marketers, please contact the author, Dr Andree Bates, at Eularis: http://www.eularis.com


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