due to that particular brand? Is there a threat or is there an opportuni- ty? What could it be? Such analysis could easily lead to further research being commissioned which could be entirely misplaced if the analysis has a flaw.
The beauty of market analysis is in the eye of the beholder As was referred to earlier in this chapter, there are many measures of a market. This is because different business functions look at the market in different ways. Consequently, market analysis is used differently by the different functions in the company. The finance function will com- pare the actual sales turnover with a budgeted figure, because the expenditure of the company has been planned against those budgeted sales. Production will think in terms of volume and whether it has the capacity to meet it, and retail buyer will be looking (in market terms) at the popularity of the items or brands that are being proposed to them, in order to ensure that their shops have a proper range of ‘qual- ity’ products for sale to customers. The operations management may be looking at sales per store and whether they are up over last year, and marketing will be looking at market share and its change over time.
Depending on the way the data is used, people in the company will react differently. For example, the sales are up over last year but not so much that the sales forecast will be reached. What implications does this have on the projected profitability of the company? Should some proposed plant investment be postponed, should the marketing be increased or cut entirely, can the deals that have been struck with sup- pliers be honoured, and what does it mean if they are not? Is the fail- ure to achieve budget ‘our fault’ or that of the market? If so, what are the implications for the future? And so on.
and this combined with the fact that much information now resides on computer has led to a growth of departmental analysis – no longer is analysis the province of the specialist. This development has also to be put in the context of the general business culture, which applauds the use of numbers to justify actions.
The sort of extra analysis that is taking place is very diverse: for example, on financial data, customer-originated data such as ‘returns’, complaints or information from customer helplines, staff data such as staff turnover rates, sickness and so on, delivery and ordering data such as ‘out of stocks’ and, increasingly, information from company Web site usage or its particular use in placing an order and tracking it.
In addition there could be mystery visitor data, routine operational reports and so on – the list is endless.
Actually, the availability of internally generated information is also on the increase from company-wide systems, rather than just depart- mental ones. Although computer systems have been employed increas- ingly to run different aspects of organizations for some years, the data that they could implicitly capture has often not been available for anal- ysis. This is because the people responsible for designing the IT systems used for running the company have historically not been sympathetic to designing the systems to be useful for data analysis – they have had more taxing matters on their minds. However, there are signs that this is changing, and that a more modern and enlightened view is increasing- ly being taken. The net effect of all this is that more and more information will be becoming available. Obviously, this opens up real opportuni- ties, where companies are prepared to put in the investment necessary, but also further opens up the possibility of data indigestion.
Responsibility for analysis of the new data:
the new quantitative
As can be seen, having the ability to conduct comprehensive market analysis is of great value to a company, but it is neither easy to do nor ultimately necessarily unambiguous. The issue that is emerging with the new data is that the analysis of it, such as is taking place, is mostly not in the hands of professional analysts. Furthermore, departments can be quite jealous about giving access to their information to others in the organization, and system-wide data is generally not available at the moment. Consequently, there is a real problem that the increasingly
available new information will not be used to the advantage of an organization, simply because of politics and lack of recognition as to the complexities of undertaking such a task. Furthermore, due to the competing perspectives that the different analyses give on the organiza- tion, the problem is likely to be a breaking up of the information climate, which is really bad when the company wishes to take strategic deci- sions. Broadly speaking, an entity in the organization needs to take control of all this, and in part this is what the new ‘customer insight departments’ could be all about.
Because internal information is abundant and cheap and external information is limited and expensive, it would seem sensible to examine how internal measures can be used as surrogatesfor real cus- tomer information, and external market research used to calibrate these surrogates.
Ultimately, this will occur by the application of mathematical meth- ods, many of which are really quite simple. The need will be to use the internal measures as surrogates for consumer behaviour or thinking.
For example, the levels of product returns are a direct indicator of unhappiness with the product, but what do different levels of return actually mean in terms of the users’ relationship to the brand, and are these returns coming from different people or from the same people?
Obviously, consumer research can be used to calibrate this.
Furthermore, the rate of change of the level of returns could be used as an indicator of a sudden effect. This could be as simple as the product having a defect that had not been picked up in production, or more interestingly, that the market expectations had suddenly changed, per- haps by the launch of a product by a competitor that has changed the perception in the market.
A very simple way of dramatically levering a simple internally gen- erated figure is to link consumer behavioural data to sales data. A powerful example of this can be illustrated by a retailing case. When a retailer counts the number of sales that it has made, this is not the same as the number of customers it has, as some may have purchased on a number of different occasions. The way to calculate the number of cus- tomers is to divide the sales transaction data by the mean frequency of customers, a figure that can easily be obtained from simple research. By linking a survey to the actual transactions it is possible to divide the sales across customers of varying frequency, and those who have shopped for the first time. Doing this on a regular basis means that the
numbers and values of customers can be tracked over time. So, for example, the effect of advertising can readily be monitored – after a campaign, the number of new customers would be expected to rise, the frequency of well-known customers might increase, the mean spend of certain frequency groups might change and so forth. Simply asking every ‘nth’ customer at the till about his or her frequency of visit and entering this data into the till could transform the understanding of the sales performance of a retail chain. Opportunities of this type are many, and is one of the reasons that quantitative research is going into a new period of importance.
It is probably true to say that those companies that embrace these ideas both fully and formally and who focus their attention on building up good pictures from internal data and augmenting it with market research are likely to be the winners in the future. One might even give it a name: ‘the new quantitative research’.
So we can expect to see increasing attention being paid to the grow- ing amount of non-classical internal data, and the analysis of it being subsumed into the insight departments where it can be put alongside classical market research and market analysis and its value augmented by consumer calibration. Achieving this will be a real battle for the future – and a bloody and political one it will be too.