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The highly personal business of Medela

The highly personal business of Medela
Capitalizing on Insights 5min

The highly personal business of Medela

The second annual Chicago Executive Insights Roundtable took place on September 14 2018, bringing together guests from Abbott Laboratories, Astellas Pharma, Bristol-Myers Squibb, Cardinal Health, Glanbia, KAO, Medela, Mondelez, Motorola, Pepsico and Takeda.

Host Paul Bould, VP Global Marketing Insights at Abbott, welcomed new attendees and returning guests to the 2018 Chicago Roundtable. He introduced Jill Hunt, Director of Global Strategic Insights at Medela, who presented a case study on knowledge management.

Medela manufactures breast pumps for mothers, in what Jill describes as a “highly personal and emotional business.” Unlike many other brands, the business is defined by a short window to reach potential customers: expectant and recent mothers are their only clients, so continuous, long-term relationships are rare.

At the same time, the path to purchase is ever-changing, meaning new ways to get products to consumers are constantly being explored. This means insights are a highly valuable resource that must be used in product innovation and communications.

But with 3000 employees, headquarters in Switzerland, and offices in the US, Germany, and China, Medela faced a significant knowledge challenge – siloed knowledge that was tough to find and share. According to Jill, “Our knowledge was everywhere, but nobody was sharing it. So there were simply too many instances of duplication.” To tackle these issues, Medela partnered with Market Logic to roll out the Knowledge Exchange.

As a centralized knowledge sharing platform, the Knowledge Exchange houses all of Medela’s accumulated research and learnings.

Launched in August 2016, the platform now contains over 500 research projects, a thousand consumer voices, and more than 230 concept tests. Knowledge Exchange customers, as Jill sees them, are business stakeholders with diverse needs, from insights managers and marketers to R&D teams and salespeople.

Jill shared a few examples to bring the benefits of the platform to life. When Medela’s CEO needed a report on a strategic initiative, Jill used the Knowledge Exchange to generate it in record time. Previously, a report for the CEO would have taken days of manual effort to prepare, carefully reviewing all available reports and painstakingly retrieving research findings and data from individual projects.

Instead, Jill conducted one search on the topic, compiling findings from 36 different research projects representing almost $1 million in research investments. She used filters to refine her results and then added commentary. The report came together in under an hour.

The audience asked how Medela manages knowledge sharing across departments. Jill detailed the security levels used to control access, so the right people get instant access to the information they need.

Another topic focused on cost tradeoffs between investment in the platform and the budget for new research. “Was it hard to convince executives to back the decision to invest scarce research dollars in a knowledge management platform?”

Jill said there was an appreciation that the challenge wasn’t about creating more research: “We already had a lot of valuable research. We just needed easy ways to get it.” For example, when the CEO requested research on a planned innovation, Jill was able to put a stop to it. Her team already knew the answer from past projects and was instantly able to produce the relevant insights.

Jill said care was taken to introduce the platform and continuously promote it internally, but
once teams realized how much it would simplify things, it became an easy sell. European teams used to have to wait for Jill, who is based in the US, to answer their questions at the end of the day. Not having to wait until she wakes up makes life a lot easier for them: “Now, the answers are always at their fingertips, 24/7.”

Jason Childers then took the stage to talk about next-generation market insights platforms. These use AI to help users find answers quickly and easily, by connecting and learning from all data. They aggregate data by compiling elements from multiple files in a summarized answer.

But it goes further. Jason explained that semantic content from every data source is extracted and built into a knowledge graph representing the user’s industry. This helps the machine understand what happened and why to uncover new insights.

Jason said that the platform’s ability to understand unstructured data (powerpoints, social media, videos, etc.) is one of its greatest strengths. Computers often have problems with this task, but sophisticated machine learning techniques can teach software to “read” through these files.

For example, let’s say the platform contains several reports with findings about men in Poland using different personal care products when they shower in the evening. The platform can then state, with a high degree of confidence, that in Poland, men are likely to shower in the evening.

When the user asks a question, the platform looks at every word for connections. Each element of the search engine is supported by AI: users are given a “best answer” from all synthesized data, while the semantic search finds what users mean rather than what they say. Key findings are automatically summarized. The platform can also visualize data in charts and graphs, turning structured data into easily understood diagrams.

A good platform works with existing processes in every department of the organization, from R&D to marketing to sales. As marketing actions occur, feedback from pre-launch tests and post-launch reporting are fed into the knowledge graph, creating a continuous learning loop for insights.

As the platform is configured and the data model perfected, it begins to understand the user’s work on an even deeper level. The machine will see turning points where insights can make a difference, and suggest them to users. This “artificial coworker” will help insights professionals to make the right decisions at the right time via easily actionable suggestions, for a truly next-generation experience.

Paul Bould responded to Jason’s presentation by noting that Abbott sees the new Market Insights Platform as an opportunity that will “bring effectiveness and drive competitive advantage quicker.” He then led an open discussion on the impact of AI on insights management.

Curious about AI’s ability to differentiate between sources, attendees discussed how the machine dealt with conflicting information: for example if two reports give a different percentage of market share.

Jason said that the AI algorithm will then present the best choice, based on relevance and recency.

Attendees agreed that combining structured and unstructured data was an excellent idea, as machines have typically had trouble with this in the past. But bringing more data from more sources to one platform obviously brings new challenges, in terms of data volume and contextualization.

Jason said that, for a next-generation platform, the more data, the better. Each platform starts with an industry model, which gets fine-tuned over time. As data is harmonized, clear answers emerge. Paul pointed out that “This is what drives decision making: when you bring the right people together and go through the data points in a structured way. AI makes getting to that point faster.”

With the 2018 Chicago roundtable at an end, attendees adjourned for a networking lunch.

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