The rapid pace at which the world of life sciences is moving and evolving is driving the production of increasingly diverse and unstructured data. As a result, the number of sources and the volume of accessible data is expanding, fast. However, we would argue that the internal capabilities to make sense of it – the process of transforming it into actionable insights that can drive better decision making and long-term business value – have not fully kept up in most companies. This is especially true for you working in commercial business functions such as marketing and medical affairs and are dealing with an increasingly complex network of external stakeholders and market influencers.
Here are three ways that artificial intelligence (AI), when applied in the right way, will help you to faster obtain more comprehensive insights about key stakeholder at a lower cost.
1. Target the right stakeholders – advanced analytics to drive better decisions
No matter if you are looking for members for your scientific advisory board, thought leaders to broadcast your message or future customers that want to buy your products – you want to target the right stakeholders. This is often easier said than done. Below, we’ve broken down the benefits of a sophisticated AI approach by two commercial use cases, marketing & sales and medical affairs.
Drive marketing and sales
AI comes with the capabilities to structure, classify and index vast amounts of data. Because of this, you take a range of different data sources into consideration at once when you decide on how to best prioritize your marketing and sales efforts. When applied in the right way, this will arm you with the capabilities you need to apply highly advanced segmenting on a global scale of potential customers.
Now, let’s make this more hands-on and look at the data source you could be working with and how you should be using them. In the universe of life sciences – from our experience – some of the most useful data types include scientific articles and poster, grant payments and their recipients, clinical trials, guidelines, social media, traditional and non-traditional news sources. Below are four steps that outline why these are important and how to create actionable insight from them:
- Use aggregated data from articles and posters to analyze your market and uncover insights into which research segments are growing the fastest and the topics driving the trend in those segments. Use these insights to decide where to focus your efforts and create meaningful, engaging and highly targeted content for your marketing and sales funnel.
- Create an ideal customer profile that finds value in your offering, is of great commercial potential and a likely buyer of your products. Examples of such could be: recently received grant funding in target segment, junior researchers executing experiments vs. senior decision maker, people publishing using an incumbent technology etc. Based on this, build highly targeted lists of people corresponding to that profile.
- Monitor global scientific, funding and news activities within your niche area of interest continuously to identify activities that should trigger actions in your organization. Maybe some of your previous buyers or shortlisted key prospects recently received funding.
- Voila, with the above approach you know WHO you should be speaking to and WHY as well as WHEN you should reach out and WHAT you should say – the wet dream of every sales and marketing department. As you execute on your strategy, make sure to track all your actions and implement a scientific approach to your outreach:
Observe => Hypothesize => Test => Repeat
Look at the data to pivot or double-down on what’s working in your customer profile, messaging or content etc. This ability to analyze unstructured and otherwise siloed data at scale, which AI enables, gives you the tools you need to become a true sales and marketing powerhouse and reap the benefits of a targeted and data-driven approach. One of our customers in the research instruments space executed a plan similar to this for an upcoming product launch which led to a factor 5 increase in email opening rate. More importantly, they closed deals 70% above target.
Boost Medical Affairs
Different sets of data all tell different stories about a stakeholder of interest – the scientific profile gives you a sound idea of the expertise, while previous corporate engagements, collaborative network, advisory board & societies might tell a different story and give you a more deep understanding to support key collaboration decisions. Most likely your preferences and specifications vary with context, as they should. Now, being able to identify this data and extract it from the drastically different, siloed sources in which they are stored is a full-time job in itself. Manually structuring the diverse information and mapping each activity to the right stakeholder is damn near impossible to do, even at small scale. Imagine applying the same process for the global landscape and then re-doing the same thing each day to keep the data fresh and updated – you would need an army (and pay for it). As a benchmark, one of our clients was used to running queries in multiple publication databases and manually extracting and analyzing.
If you apply AI in the right way, it will bring you structured, fresh and updated actionable insights (remember, there’s no point in the data itself) that can improve your decision making. The power of automation and AI will enable your organization to consider huge volumes of information in your decision making, instead of spending the majority of your time just to gather, structure and make sense of the data. We are fortunate to be working with many of the front-runners in the life sciences industry and we’ve seen a shift in the last two years. Gone are the days where you limited your stakeholder engagement to your existing network, or looked no further than well-known experts in a therapy area. In progressive companies, it has been replaced by a data-driven culture that strives to produce better, close to non-biased results. Such an approach provides a huge competitive advantage today and lays the foundation for a structural competitive advantage for years to come.
2. Decrease costs – save the millions you spend annually on purchased reports and data sets
Life Sciences companies spend somewhere in the range of $15-$30M on consulting engagements and reports each year. In addition to that, different functions of the organization purchase various raw data sets that, when accumulated, become considerably more costly – ranging upwards of $70M+. As an example, many of our clients pay close to $50 000 for a single KOL mapping project. The deliverables of these reports generally feature static data that degrade over time.
Sure, these reports and data sets serve a purpose and provide value. But, they tend to be immediate, point-solution consulting and purchases that solves an immediate need but does little for you in the long run. At a minimum, you should strive to be data-driven and order custom reports in a more stringent and efficient manner to improve the outcome and save money. This segway’s me into my third point – speed, operation excellence and long-term competitive advantage.
3. Increase your speed and operational excellence
To create a long-term competitive advantage, you need a continuity in your approach. Sure, purchasing lists of opinion leaders, raw lead or potential investigators from consultants just like you always have done might be comfortable (yet pricey). It will not, however, give you a long-term competitive advantage in this rapidly evolving landscape. Not against your data-driven rivals. We see this time and time again – purchased insights (of questionable quality) stored siloed in the organization, duplicate data sets purchased by different divisions with no information transparency and completely non-harmonized workflows that make it impossible to track performance and ROI across teams. The advances of machine learning and AI open up a spectrum of new possible approaches that, when applied, can create positive feedback loops to different parts of your organization. But not everyone is ready to change. In fact, based on a survey with executives from Fortune 500 companies, NewVantagePartners listed cultural resistance to change as the biggest challenge for successful business adoption of AI. Are you ready?