5 ways web data can help retailers

If you’ve been paying attention to the news at all in the last few years, you’ll have heard the phrase “Big Data”. And if you’re like us, you’re probably getting pretty sick and tired of it. You certainly don’t need us (or anyone else) to tell you the importance of using data in the retail industry – you’re already using data everyday! Sales data, customer data and product data are all familiar to you and your analytics team.

What’s missing from this equation isn’t Big Data, it’s web data. Your sales, customer and product data all come from within your organization – which is obviously very useful – but it’s only half the story. There is so much more you can learn if you look outside your organization at the data that is on the web. This data has only recently started being accessible to retailers, but already it has made a big impact.

Here are five ways you can start using web data today! They’re all based on real-world examples of clients we’ve helped at import.


Pricing strategy

Knowing how to price products for the best margins requires you to constantly reevaluate and adjust your pricing strategy in response to market trends, customer behavior, stockroom levels and a whole host of other factors.

As I mentioned before, you are – or at least you should be – already measuring and basing these decisions on your internal data. But, a large part of your pricing strategy should be determined by what your competitors are charging for similar products.

Just think what you could do if you could pull product and pricing data on all the products, from all the brands in all of your markets every hour. You could run that data through an analytics tool like BigML or your own internal software to spot market trends and make pricing decisions. This analysis can help you figure out how much to charge overall for your products, which features you can charge a premium for and when you should be discounting. Having a more complete picture of the competitive landscape will give you a much more stable platform from which to make pricing decisions.

Campaign monitoring

Many brands are turning towards more creative mediums to promote themselves. The trend in video marketing has become especially popular. Sites like YouTube are great for providing statistics on things that are easily measurable like the number of views, but these metrics don’t necessarily indicate that a campaign is successful. You need to know more than just how many people saw your campaign, you need to know what they thought about it.

To do this, you need to extract all the comments from your (and your competitors’) videos. Then you can run the text through some kind of sentiment analysis software to get an idea of how people feel about the video. This analysis will provide you with a feedback metric to measure current and develop future campaigns.

You can also run this type of analysis in other places as well by collecting customer reviews off third party websites, social media mentions on Twitter and Facebook, as well as analyzing the response to news coverage. If it’s on the web and your customers wrote it, you should be trying to collect it.

Demand analysis

A major contributing factor to which products you invest your time and money on is the projected demand – how popular you think the product will be with your audience. Your own sales figures are a good starting place, but often by the time the customer has landed on your page it’s too late. You need to have a better idea of what demand will be before you launch (or even build) the product in the first place. There’s no point investing millions in a product that won’t resonate with your market.

Online classified sites are a good place to monitor what people are searching for and which items are most popular. Using web data you can monitor posting rates in particular categories and then based on those rates you can prioritize the categories, products and regions to focus your efforts on.

Channel partner management

If you’re one of the many companies who sell your products indirectly through channel partners, you’ll know how hard it is to make sure they observe your minimum retail price. Because prices for online retail have become so easy to change (sometimes they change hourly), it is possible for channel partners to hold a flash sale for an item for an hour on a Friday afternoon and then return the price to normal before the manufacturer becomes aware.

Some sites like Amazon even agree to a margin up front (if they buy from you for $100 they want to sell for $150) and if they have to discount due to a competitor discounting, they will charge the wholesaler for the difference. This kind of discounting can be quite damaging to your bottom line.

Using live web data, you can monitor all your channel partner’s websites in real time and know immediately when someone is not abiding by the MRP.

Grey market policing

Another popular use of web data is for those who sell products via resellers. Generally, these resellers are not allowed to sell through third party sites like Amazon or Ebay. Unfortunately, the only way to find out if they are or not is to search these sites for each product manually. This is obviously very time consuming and ineffective.

With the help of web data, it is possible to automate the process of searching for these products – making it a lot faster and more efficient. That way your legal department can identify infringing resellers instantly and even issue enforcement notices automatically.

Getting started with web data

These are only a few of the many ways retailers are taking advantage of web data to help them get a more complete picture of their market and gain some very real value as a result. However, getting access to this data can be challenging.

You could collect it manually, but as we’ve mentioned before, that would take a very long time and would likely be incredibly boring. If you are technical, you could write a web scraper to do it automatically, but scrapers are very complex to build and not very reliable over time. If you’re not technical, you could use one of a number of scraping tools to collect this data automatically without actually having to write any code.

All of these methods, however, are pretty time consuming and require you to do all your own data cleansing on the other end. Your data teams should be busy with analysis not collecting and cleaning data. Which is why we recommend using a data service that will deliver up-to-date, accurate and pre-cleansed data direct from the source to your data teams in a format you can work with.