Don’t ignore the Data

The most powerful data scientists are those who act as bridges between insights and people [Forbes.com]

Big data is not only driving how we deliver better patient outcomes, but it is also playing a more comprehensive role in shaping go-to-market strategies. Data enables more targeted marketing activities across personal and non-personal channels; this more relevant content at the right time can result in better engagement. The deeper the insights into customer behaviour and preferences, the more on-target subsequent marketing can be.

Within Commercial Excellence, Business Intelligence [BI] teams are armed with a set of skills and tools to produce the required retrospective marketing and sales reports. However, excelling BI teams go beyond displaying data in relevant formats, to proactively generating marketing and sales insights for their commercial leadership and brand managers to act upon.

Analytics can increase a marketer’s ability to go beyond execution and put more focus on developing deeper customer relationships, optimising each touchpoint through the customer’s journey. Analytics can help gain a greater understanding of the depth and quality of our customer engagement. Analytics can help make it possible to deliver more consistent multi-channel experiences across all channels. And analytics can also provide insights into what content and channel are the most effective. Making good commercial decisions involves highly skilled marketers partnering with BI, taking the guesswork out of decision making.

Why aren’t more companies creating great customer experiences?

You might be surprised, then to hear that the number one concern we hear from senior leaders is that marketers are either not looking at the data or are not benefiting from it. What could be the reason?

First, some organizational change and upskilling are required. The reality is that digital technologies and data won't transform a business if its commercial operating model isn't designed to accommodate the new digital activities and the multitude of data collected.  Brand teams working independently and outsourcing various digital activities to agencies, including the data collection and analysis, prevents in-house analytics across brands and deep insights into the customer journey with the company.

In order to produce the analytics needed to enable informed multichannel marketing strategies and successful tactics, the BI team requires new capabilities and internal partnerships.  The team needs to be multidisciplinary and a strong partnership with IT.  The Commercial Excellence leader, the BI leader and the IT leader, can only be successful in collecting the right data and producing relevant and timely analytics, if they have a shared vision of success, and unified commitment to analytics and insight discovery.

Additional challenges

Challenges preventing good analytics in pharma include:

• Data silos [i.e. data held in medical, marketing, salesforce CRM, R&D etc];
• Dirty data not in a manageable format;
• Legacy systems, processes and technologies preventing integration of data
• Lack of investment in the right technology to collect and display data.

Moving forward

Steve Olenski a Forbes contributor, recently made some recommendations for marketers, that can also be applied to Commercial Excellence. We can take advantage of data to add value to the business by:

• Helping to break down the data silos and getting the data centralised;
• Keeping data streams current – data to be actionable needs to be real-time;
• Secure investments in the right tools and technology – data can only be collected if you have the technology to do so.

BI will need to automate data analytics in order to deliver relevant analytics fast enough to meet the demands of the decision makers. And across the organization capabilities must be developed to review the data, draw insights from it, and consistently make data-driven decisions.

So do not let analytics be an afterthought. Quality implementation of systems and tools requires upfront planning and training, but also ongoing investment and coaching to produce meaningful analytics and draw maximum benefit from the data available. However, the transformation has begun.

McKinsey offers some good advice in tackling this big challenge:

Start with the hypotheses; use that to guide data and analytics, but focus only on what is relevant. Formulate hypotheses and business questions early, and use that to guide data collection and analytics. Data are always messy to integrate and no company has perfect data, but it’s important to not let the perfect be the enemy of good… Move quickly to refine the hypotheses and generate insights through analytics, then iterate. [McKinsey]

Article Contributor: Melanie Brown, Managing Partner