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Data-Driven Brand Protection


Today, more than ever, businesses are using data analytics to gain efficiencies, manage work processes, track sales, target specific markets, and many other use cases. From small niche businesses to global organizations, operational and market data is available and being put to work to help organizations understand and interpret meaningful patterns and make informed decisions. Brand protection is no exception to the value data analytics can provide. Through data analytics brands obtain clear actionable insights and drive business intelligence creating an effective overall strategy against illicit activities such as counterfeiting and grey market diversion.

Those involved in illicit activity attacks against brands are non-discriminatory and have one focus in mind – profit. Historically, high-value high-quality products have been attacked but today any fast-moving consumer good (FMCG) is a target. When we met a large consumer product goods organization several years ago, they did not have a brand protection program in place but feared significant impacts/threats existed. At that time, the policy was to have consumers send suspected counterfeit goods back for investigation. As well, the brand was making purchases of suspected counterfeit goods from eCommerce and Social Media sites. Unfortunately, this only provided limited answers to the overall scope of the issue. On a singular basis they knew where counterfeits were purchased and sent but could not answer other important questions and therefore could not address the problem at scale.

To look at the types of questions a data-driven brand protection strategy can answer we must first look at three key elements involved.

  1. Types and volume of data collected. The volume of data collected will be in direct correlation to the methodology deployed to collect. It is vital to make it as easy as possible for all stakeholders to participate and thus generate the largest amount of data. See our blog post, The Value of Serialized QR Codes, to better understand why an open and easy-to-use consumer methodology offers the best solution.
  2. Immediate real-time actionable insights obtained. A brand protection strategy incorporating data analytics will result in insights and alerts that significantly expand the type of questions a brand will be able to answer.
      • Where is counterfeiting occurring? Originating?
      • How much counterfeiting is occurring?
      • What products are being targeted?
      • Where is diversion occurring? How much?
      • Who potentially is involved?
      • Where are stolen goods ending up?
Counterfeit Hotbeds
  1. Business Intelligence (BI) Reporting applied to data analytics and insights. BI involves the use of the data analytics and insights to drive value and help brands make business decisions – Where to start and focus resources? How best to approach? It is supported with answers to broader based questions.
      • How large is the problem? Is it isolated or widespread?
      • What do the counterfeit / diverted supply chains look like? How are these products traveling and where are they ending up?
      • Is counterfeiting and diversion trending upwards?
      • What level of disruption do we have in our market channels due to diversion?
      • Who is diverting products and to what markets? To eCommerce sites?
      • What is the overall cost of counterfeiting and diversion to the organization?

BI enables business value beyond data analytics by addressing questions relating to product authenticity and efficacy – i.e., Has the product been tampered with? Has it been transported within the required environmental ranges or subject to grey market conditions by being transported somewhere it should not have been? This is particularly relevant to life sciences consumer (and pharma) products which have public safety implications and have been subject to increasing counterfeiting and tampering.

Custom BI dashboards and reporting that provide both operations level and executive level actionable analytics, in real-time and predictive in nature, is how business value is unlocked in the data-driven approach. It allows enterprises to focus on current state whilst also allowing to predict and create value in the future and to be agile enough to adjust the course as necessary.

Grey Marketing Activity

In conclusion, the business transformation to unlock increased value and solve counterfeit and grey market diversion challenges requires a program level strategy that at its core recognizes the importance of a data-driven approach across the entire value chain. By collecting and measuring both physical and digital value chain data, across all stakeholders (including consumers), and then applying big data and analytic solutions, key business challenges that have C-level attention can be addressed effectively for the first time.

GPAS is at the forefront of protecting brands and implementing a data-drive brand protection strategy.