Despite the abundance of data available to e-commerce companies, many continue to miss out on valuable insights due to an inability to effectively analyse it, as well as failure to take equal advantage of available external and competitor data.
In this blog post we’ll look at a method Whispr Group uses to produce actionable insights which could be far better exploited by the e-comm sector – analysing open source data – and outline some of the many ways it can be harnessed to gain new learnings and a competitive edge.
One way of using open source data analysis that Whispr Group’s e-commerce customers find particularly useful is to guide product pricing. In the examples below (made anonymous for confidentiality) we analysed open data on hundreds of products from the same sphere on an e-commerce site to pinpoint the average price point in three different product categories, mapping the target brand’s pricing against those averages, as well as outlining rival brands’ pricing of their competing products in the categories. The graphs also show how premium competitors are positioned as well as budget brands.
Doing this kind of mapping manually would be incredibly time-consuming due to the crowded nature of the e-commerce landscape, but Whispr Group’s Varys AI technology makes it possible to carry out dynamic price tracking that in turn can be used for a fluid, more effective pricing strategy, where prices of products can be adjusted according to real-time demand to maximise potential gains.
Whispr Group also analyses open source data to produce useful insights from consumer reviews. Review volume for example can be used to outline the popularity of a brand vs its competitors, as well as indicate competitor sales (a percentage of sales result in consumer reviews, so a higher volume of reviews correlates with a higher volume of sales).
The result is a better understanding of brand positioning vs competitor brands in the market.
The review analysis can be taken a step further to produce an average rating from the reviews (see the bottom line of the above graphic), in this instance broken down as average review rating of competitor brands, but it can be filtered further to show average rating of competitor products if desired.
Semantic analysis can then be carried out to understand what drives positive, neutral and negative sentiments about competitor brands or products as well as your own. Better information on customer attitudes to relevant brands and products ultimately means your brand has a deeper understanding of how to engage them, not least in outlining potential opportunities to make marketing gains by exploiting competitor product weaknesses that are highlighted by the analysis!
Those are just a small number of the numerous ways open source data can be harnessed by our analysts to produce actionable insights for e-commerce companies – we haven’t even mentioned its power for lead generation, or for strategising when entering a new market for example. To find out more about the full range of potential you can contact us here using the red button below, but here’s a brief reminder of the uses for open source competitive data in e-commerce that we’ve now outlined:
And keep in mind that this is only one of the many methods Whispr Group uses to unlock untapped insights in the e-comm sphere: each method ultimately has its own unique strengths. In our on-demand webinar How to Ride the E-Commerce Boom with Data Insights we explore some further methods like combining traditional surveys with social listening, and detail the kind of insights that approach can uncover. You can watch the webinar at this link.
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