Pricing intelligence: what is it and why does it matter?

 

This article will look at the subject of price intelligence and why it matters.

 

The question, relevance and importance of “big data” and how it relates to price intelligence as well as how price intelligence can shape the performance of online retailers will be explored in detail.

 

The article will consider the question of what the pros and cons of price intelligence may be and will comment on how important a price intelligence strategy is for online retailers.

 

 

 

Price intelligence is a complex topic that involves retailers monitoring their competitors’ prices and responding to changes in pricing, often to gain or maintain a competitive advantage.

 

Price intelligence often involves data mining to identify how competitors are setting their base prices for their products. Some retailers also look at base prices for very similar products as well. However, this is a superficial price intelligence strategy, and often retailers will go further and deeper into the nuances of their competitors’ behaviour to find opportunities and weaknesses that can be exploited.

 

For example, effective price intelligence will often require deep analysis of market position, competitor behaviour and fluctuating price to create an overall picture of price nuances and strategies employed by direct competitors selling the same or similar products. Price intelligence also extends to promotions and codes that can be used to discount prices at the checkout. These will not always be very obvious from online advertisements of the product description, which can be deceptive for competitors trying to keep tabs on how prices are being set by their direct competitors. Furthermore, a common price strategy will be to offer free postage and packing and price intelligence is about knowing when these types of strategies are implemented, when and for how long.

 

Price intelligence also refers to price sensitivity, which is a focus on patterns relating to how prices change and how often. Price is often so variable that even being behind a major price change by a few hours can result in widespread losses to a company. Amazon, for example reassesses price every 10 minutes, and will reset prices to ensure they are the cheapest provider.

 

Due to the sheer complexity of price intelligence, any adjustments made on the basis of price intelligence alone usually carry high risks. Conversely, a lack of price intelligence and analysis, or following a manual approach to price setting represents a much greater risk, as it is simply not possible to gather the type of information needed to create modern price intelligence strategies without an automated approach to price monitoring.

 

These risks can make or break a business, and retailers take bad decisions based on price intelligence all the time. Retailers armed with price intelligence needs to be trained to analyse it professionally, and they should take any decisions about how to act on its basis, very carefully. The wrong analysis can lead to vast and sudden revenue losses, so it is worthwhile for retailers to invest in providing proper training to those tasked with price intelligence analysis. As such, to gain any advantages from price intelligence, retailers may notice that an initial expenditure on staff training, staff hiring or acquisition of software is necessary. Usually this pays off, “down the line” as retailers are able to gain more insight into their competitors and adjust their prices intelligently.

 

 

 

The first step in any price intelligence strategy will be to identify what competitors are also selling a given product, and on what platforms. It is important for retailers to know their direct competitors (and where they are selling their products) in detail so they can develop appropriate strategies to sell their products to consumers who hold increasingly high levels of leverage and choice. In price intelligence, some competitors will always be ranked as more important, more of a threat and therefore more in need of attention than others. Getting this “ranking” of competitors right is a major strand of any price intelligence strategy.

 

 

 

Retailers will also need to find out, and collate what competitors are selling products that are similar to their own. Each of these products will need to be evaluated on its own terms to gauge what level of threat each poses. This intelligence can be gathered through automation, for example algorithms, or through human analysis and reporting. A typical strategy will be to combine information gathered through automation with a human “check” of the information gathered to ensure that irrelevant information is discarded and relevant information is evaluated and acted on appropriately.

 

 

 

Keeping a record of data related to competitor behaviour enables retailers to predict how their pricing strategy may change over time, so a very important phase of the price intelligence strategy is information extraction and collation, and this often takes place once direct and indirect competitors have been identified and evaluated in terms of the level of threat each poses. For example, some retailers will cut their prices drastically at times when they know that a lot more people are making purchases – at Christmas for example. 

 

An important aspect of information collation will be how customers react to certain prices, so information should be collated from multiple points of view for example the retailer, and the consumer. If customers’ buying behaviour has rapidly increased as a result of a reduction in price, this is useful information for the retailer. It can allow them to make crucial determinations about a price strategy like when prices are too high, or too low, over a given period of time. As such it is important to remember that price intelligence isn’t just about cutting prices. Some businesses will fail because their prices have been set too low. Information collation allows retailers to set, and adjust their prices to the exact right price to ensure their profitability and market position.

 

A retailer that uses this type of price intelligence very effectively is Walmart. Walmart analyses point of sale data to identify patterns in consumer demand. This data can then be used to adjust pricing, so if a particular product is selling very well, it may be possible to increase prices slightly, which could result in larger profits.

 

In the case of Walmart their price intelligence is used to influence logistics and supply chain management, so forecasting that predicts a product will sell well is also fed to Walmart warehouses and stock managers who can ensure that products do not go out of stock. This then supports the profits that the price intelligence forecasting has created as customers don’t face stock shortages, which can in itself be harmful to brands.

 

 

 

Gathering information on price intelligence will never, of itself, help increase profits. Price intelligence must be acted upon and that is where evaluation, analytics and reporting comes in – these are the mechanisms through which retailers can look at masses of information, and make strategic decisions that will support growth and profits.

 

Within price intelligence, reporting activity is particularly essential to ensuring that data is acted upon appropriately. In sales, small changes in price can add up to huge losses in terms of profits for many brands. This is why people set up automated reporting functions (within their price monitoring software, for example). This means that they get an automated warning when there are price changes, or fluctuations in their competitors price strategy. The reporting can also warn of changes in promotional activity and other nuances of competitor behaviour, like whether they advertise free postage and packing. The automated reporting functions provide alerts to ensure that price changes don’t go unnoticed.

 

A good reporting strategy should always be linked to an evaluation strategy. The evaluation strategy should be focused on decisions taken on the basis of information that has been revealed through the reporting and analysis stage. It may be that a retailer, having seen a report of price drops or promotions applicable to products their competitors are selling, decides to reduce the price of certain product lines in order to quell profit losses. The evaluation strategy requires the retailer to start by defining their objectives i.e. what they want their intervention to achieve.

 

So, an evaluation strategy might start off by saying competitor x has reduced their product price by 1 GBP, so we will reduce our products by 1.01 GBP in order to ensure that customers aren’t lost to competitor x. The evaluation strategy will then revisit the situation and consider what has actually happened in the time that has elapsed, against the benchmarks and objectives set out originally. This is important, because, it may be that the first intervention isn’t sufficient to quell profit losses to competitor x, and supplementary activity needs to be carried out. This type of analysis also requires human direction and intervention, because there will always be a unique set of circumstances that could be driving competitor behaviour. The better this behaviour is understood, the better market trends can be predicted and adjusted for.

 

Furthermore, evaluation can be used to “test the waters” in circumstances where retailers don’t know exactly what needs to be done to achieve certain desired outcomes. It may be that a retailer believes they can benefit from increasing their prices for certain product lines. If this is the case, a trial of the price increase which is continuously evaluated is very helpful. After a period of time the results of the trial period can be examined and the retailer can make an informed choice about whether raising their prices is a good idea or not. This removes the use of “gut instinct” to set prices, which is something that retailers who set prices manually often rely on.

 

 

 

Big data has become a buzzword in recent years, referencing how useful the collection and analysis of data is for retailers. How Amazon uses data perfectly illustrates this point. Amazon holds on to data on just about every interaction it has with customers. It retains and analyses data relating to what people click on, add to their baskets (without checking out), add to their baskets and check out and what people add to their wishlists. In doing so, Amazon has found itself in a unique position to predict and forecast consumer behaviour and interests, based on that customer’s own past behaviour. Amazon then uses this information to create effective advertising campaigns that are then delivered directly to consumers. An example is where a customer has placed an item in their basket, without checking it out. Amazon surmises that although there was no ultimate purchase, the fact that the item was added to the basket indicates an interest that an advertising campaign (personalised to that one shopper) can be built around. The next time this customer logs into their Amazon account, they may find this item advertised in some way within their account, or this customer may find they are emailed directly in relation to the product or products they have shown interest in.

 

Additionally, point of sale data is used to influence and build marketing campaigns. This is another form of pricing intelligence and again, Amazon uses it very effectively by using algorithms to predict what other products that customers might be interested in, based on their behaviour on the main Amazon site. So, if someone places a makeup foundation in their basket, Amazon may display related items that could be or are typically purchased together, for example makeup application sponges. In this way, Amazon uses data that shows a unique pattern of behaviour to entice customers to make further purchases of items it predicts they may be interested in.

 

Controversially, Amazon also uses other retailer’s data to create advertising campaigns, and to set their own prices. These strategies have not been received well by retailers who argue that this is an infringement of their privacy because this information specifically relates to their businesses (and as such is owned by them). On the other hand, this information is in the public domain so, precisely why Amazon can be prevented from analysing it remains debatable.

 

This topic has become particularly controversial since Amazon began to develop their own range of “own brands” which are priced lower than most rival products, because this development has, uniquely, led to Amazon becoming a competitor to its own customers who use their site to market and sell their products. Such has been the level of controversy that this activity has caught the attention of regulators, with analysts predicting a sharp decline in Amazon profits and market share, contingent on the outcome of these investigations.

 

 

 

There are some factors that are not directly linked to price, but keeping an eye on them is nevertheless an important part of price intelligence. One of these factors is stock.

 

How often a retailer is “out of stock” of a certain product is such an important determinant of market position that it was one of the factors that Which? magazine took account of when conducting research into which were the best and worst online retailers of 2018. The best online retailers were found to be those who not only offered low prices, but those who could replenish stock quickly and didn’t advertise products when they were in fact, unavailable due to a lack of stock.

 

Stock analysis is therefore an important thread of price intelligence, because price, when looked at in isolation can be very misleading. A retailer might be cheaper on price, but when you investigate further and attempt to buy the “cheaper” product, only to find it is not available, and hasn’t been for 3 months. At this stage the price becomes irrelevant because this retailer is not able to deliver the product at this price.

 

As such, stock analysis allows retailers to identify competitors who set prices a certain way, but are not able to deliver products. These “competitors” need to be taken account of as a class all of their own because, in many cases their “price points” are irrelevant when they can’t deliver the products in question at the advertised price.

 

 

 

It is very difficult to get a price intelligence strategy correct, without making an overall assessment of competitors, their activity and the level of threat they represent to a given retailer. This “picture” of every competitor allows for bespoke strategies to be built in relation to every competitor. For example, pricing becomes irrelevant if a competitor is losing sales because they have made a gaffe or experienced a storm of negative publicity about something. Ethical issues surrounding supply chains, have, in the past led to huge losses for retailers and in these situations, price was not a determinant of the retailer’s profitability. However, a retailer setting price strategy based on their assessment of other factors, without taking account of major disruptive events like ethical calamities will lose out because they may overestimate the selling power of that competitor, at that given time. If this is reflected in their pricing strategy, they will have missed an opportunity to increase their prices, without losing customers.

 

 

 

As we have seen discussed price intelligence involves gathering a plethora of data about competitor price decisions and analysing them in depth. It matters because it allows retailers to gain competitive advantages, based on price strategy and as such, gain a larger share of the market for a given product or product line.