Innovating with Analytics
- John Morrell
- July 25, 2021
Great organizations are constantly transforming their business, and in today’s data-rich world, market leaders are innovating with analytics. Incorporating analytics into existing business models or building innovative data-centric models opens up further possibilities for businesses.
Data often reveal unique opportunities for growth in new markets or creates a competitive edge by supporting a positive and more personalized customer experience. The following strategies could be key differentiators for your organization, especially in today’s highly competitive business environment.
3 Ways of Innovating with Analytics
1. Supporting decentralization
More businesses embrace decentralization because having small groups work on a specific problem ensures that people with the right expertise develop a solution. The main downside of decentralization is coordination, an issue that analytics can address.
For example, Red Wing Shoe is a retailer that specializes in work boots. This organization collects data at POS across its 517 North American stores to analyze customer profiles and shopping behaviors.
The franchise’s analytics strategy supports decentralization by conducting a region-specific market analysis that looks at factors like potential customer profiles and the presence of trades that require safety footwear. Incorporating country-specific industry requirements for safety footwear has helped the brand develop its presence in Canada.
Using analytics to support decentralization results in a more agile business model and makes sense for companies with different geographic regions. Analytics makes small groups more reactive, saves time, and empowers middle and lower management and employees.
2. Implementing platform analytics
With platform analytics, data and insights are incorporated into business processes to enhance decision-making and identify new opportunities.
Bridgestone is a platform analytics success story. The company relies on real estate data to identify the best locations for new stores, sales data to manage inventory, and HR data to allocate employees to its different locations.
The automotive franchise uses driver data to reach out to car owners to get them to come in for maintenance and has developed data-centric digital tools for fleet management.
Another great example, CVS, is showing how platform analytics can transform the customer experience. The retailer uses a data-driven segmentation strategy to categorize the people calling customer service into six behavior groups. A scoring system identifies agents’ strengths and weaknesses and assigns them to the behavior groups they’re most suitable for. This approach reduces the call time, improves outcomes and customer experience, and supports human interactions.
3. Developing data-centric business models
A data-centric business model aims to seek relevance and competitiveness through optimized processes and data-driven decision-making. The purpose of data-centric organizations is to offer a new value proposition that wouldn’t be possible without the use of analytics.
The Coca-Cola Company has been an early adopter of AI and has been using analytics to stay relevant and delight customers with new products.
For instance, data from self-service fountains where patrons can mix their own drinks and add flavor shots lead to invaluable insights into customers’ tastes and preferences, which resulted in the creation of the Cherry Sprite soft drink.
Analytics is also used for data mining on social media to find mentions of the brand. The Coca-Cola Company uses image recognition to identify its products in images shared on social media. This data is used for targeted ads that drive conversion rates.
We’ve discussed three ways companies are innovating with analytics and show some specific examples: decentralization, platform analytics, and data-centric business models. And of course, there are additional ways to innovate with analytics.
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Datameer helps you innovate with your analytics by allowing your teams to combine and transform more data for datasets that provide more in-depth analytics.