'Price Intelligence and Web Analytics for Better Conversion Rate' webinar synopsis

Nick Tikhomirov, Client Development Manager at Competera and Andrey Sukhovoy, Head of the Analytics Department at OWOX discuss how competitor prices affect retailers’ sales and how to read and understand combined retailers’ data.


Price Intelligence and Web Analytics for Better Conversion Rate

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Why is this topic is important?

Retailers need precise and genuine data about their business and market: sales, stock, marginal revenue, customer price perception, competitor prices, etc... Data is important, but real value has conclusions you can make with its help.

Quality data can give answers to questions like “Why is CPA increasing?”, “What products should we choose for the e-mail promo?”, “Is the reason for sales decrease our high prices or online traffic is too low?”, “What price should we set to achieve our monthly/quarterly sales projections?”, and so on, and so forth.

These particular conclusions help the retailer create an invisible competitive advantage that is hard to copy, and contributes to keeping business profitable in the long term.

Pricing tasks

Product price on virtual retailers’ “shelves” affects conversion just as much as page SEO- and UX-optimization. Mainly, the price creates an image of the online-shop and its brand picture.

Nick Tikhomirov, Client Development Manager at Competera

There are few evidential grounds for this statement. A PWC survey and different customer behavioral forecasts say that 60% of shoppers talk about an optimal price as the central factor in choosing the shop. The primary focus here should be on this keyword “optimal.” They do not say “low price” or “high.” This is a good sign for all retailers because it includes not only product price, but delivery, brand weight, and everything else that are interconnected with their perception of a shop.


60% of online shoppers choose a favorite retailer because the price is right

Meanwhile, market leaders are companies that joined in at least two of the three ellipses/segments: optimal prices and full assortment or alternatively, prices and brand trust.

Thereby, the task that’s based on data pricing resolves to show the optimal price for a particular customer, in a particular store, for a particular set of goods. Pricing weight with:

Data collecting and combining

Before the data processing, its collecting, cleaning and consolidation should be done.


Qualitative data is the basis of high-quality solutions

The retailer needs to watch competitors’ data not only from marketplaces, but the competitors’ sites directly with product matchings and assortment updates. If the data were to be crawled exclusively from marketplaces, one would never find the correct info about stock availability, products on the ‘shelves,' etc...

It allows retail management to apply effective tactical steps, such as ‘holding’ specific products until the ‘stock is not available’ moment to lift profit margins. Even so, this example is one of the most frequent, it still requires accurate competitor data at the SKU level.

The next milestone is market channels data. Which channels retail uses and how they affect sales: price comparison services’ customers reacting to prices in a different way than the Google CPC audience. By calculating and analyzing marketing channels and their audience, retail can adjust its pricing strategies on the fly.

All this data can show clearly how competitors’ prices affect sales:


What was the price at the competitors’ sites at the moment a user viewed the goods on your site?

The Category Manager needs to see, break down, and analyze competitors’ prices at any moment, for every SKU and per every customer action. Data quality and specifications determine the reliability degree of conclusions made in their foundation.

The later scheme of data collecting and proceeding is time-tested: Google Analytics used globally as a customer behavioral data source, Competera collects competitor prices from different sources with the retailer’s ERP and CRM system information, and OWOX visualizes it.


Data movement

Of course, retail can be used in many different ways to gain unstructured data to work with. Every one of them has their own limitations. Below are freemium Google Analytics Core V3 API limits:

  1. Data need to be aggregated, and combined with specific metrics.
  2. There is no option to get real-time data. For huge projects, the delay amount can be counted by days which reduces the data value.
  3. It is impossible to get a deep level of data, i.e. Client ID + SKU because customers are surfing dozens of products, and the free limitations of Core V3 API get rapidly exhausted.

Another common data mining methods also have their limits:

1. Impossible to get price timestamp on the SKU level

Understanding competitor’s product price, at that particular moment, is a foundation for making correct conclusions about customers’ reactions on a certain price.
Technical limits of the data renewing frequency give no warranty that the customer saw this distinct price on the competitor’s site. This means that there is a tiny chance to define whether this price affected buyer’s decision.

2. Impossible to control data completeness

If there is no product, say at the marketplace under monitoring, there is no possibility to watch what really happens with the product: Is it absent only at the marketplace, or at the market in general.

3. No historical data

To build correlations, e. g. demand curve, there is a need for historical data for the long term. It makes it possible to compare it with the product’s views and sales. Usually to keep data for such a period tremendous amounts of resources need to be used.

4.No quality warranty

To reach the conclusions business decisions can rely upon, the retailer needs to be assured of the quality of the received data. It can be guaranteed by the suppliers’ SLA.

5.No way to get quick product matchings

Last, but not least, is the speed of new data delivery. Customers are always interested in new products, so retailers needs to equip themselves with “Johnny-on-the-spot-speed” new product appearance.

Data analysis and findings

If you have correct data, the retailer can inspect how specific parameters are affecting sales. Here are some examples of findings:




You can see that a 7% discount gives a peak conversion rate, though it can be used as the default discount for promotions.


The dependence of the conversion from price deviation

To see the graph (X-axis shows a competitor's' prices deviation, Y shows products and users proportion):

  • Assortment distribution on the price deviation segments (blue line)
  • Customers quantity who was surfing specific products with particular difference in price (orange line)
  • Purchases (red line)
  • Correlation between price deviation and spreading of gross profit

Income distribution


Define the prices among competitors and dependencies of each of marketing activities

From where did new and loyal buyers come from?


Define competitive prices and activities influence on sale and right prices on-the-fly within Price Index report (Competera).


Reduce prices to fit the market level

The list goes on and on... Genuine and correctly shown data gives the retailer an ability to forecast turnover for different product categories as well as single products.

Summary

Now you know all the advantages of data combining and visualization, competitive pricing and conversion rate growth, how to adjust the whole process and what conclusions can be made in this foundation.
Meanwhile, the most important are the ability to use this data daily. If you need any help on any of the described stage, do not hesitate to contact us:


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