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Defining Analytics

Analytics has evolved rapidly in wholesale distribution channels, fueled in part by the explosion of technology innovation over the past several years, but also as a part of the industrys natural lifecycle. Companies that keep building their analytic capability are staying ahead of the wave.
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Analytics has evolved rapidly in wholesale distribution channels, fueled in part by the explosion of technology innovation over the past several years, but also as a part of the industry’s natural lifecycle. Companies that keep building their analytic capability are staying ahead of the wave.

I find the following chart useful to think about how fully a company is leveraging analytics across its three primary functions. It is widely used (and argued about) by many analytics professionals and attributed to IBM; it is a good benchmark for thinking about ways to keep growing an analytics culture and thinking in your organization. There are many ways to push the envelope with small projects to build small successes.

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Descriptive analytics are what most distributors live and breathe – ERP exports, Excel analysis, dashboards to slightly deeper comparative tools that give visibility into what the data say happened or is happening.

Predictive analytics can’t tell the future, but it can combine historic data with some modeling to forecast a future state or likely outcome. Data mining can build correlations between datasets.  Example: Based on your sales history of Widgets into Toledo, it’s possible to profile other markets or similar types of customers. Combining data from transaction history to identify likely cross-selling opportunity is another example.

Prescriptive analytics typically builds on the first two levels of analytics to suggest a course of action based on why something is happening. Traditional examples include inventory optimization, pricing and profitability analysis. This is also the playground of big data; more complex models can create scenarios or alternate outcomes from larger and in some cases more unstructured data sources, such as online digital content, video, social media.

This industry is transitioning from a traditional outside sales-driven distribution model to a multi-channel model. Service, knowledge and product quality are no less important factors to differentiate today. But how customers find and engage with your company is changing rapidly. New competitors on the block – whether e-commerce only or more traditional – use analytics strategically to target opportunity and identify where and how they can best compete for market share.

Where is your company on the spectrum of analytics adoption and thinking?  

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