The pros and cons of using end to end software in market research

Market research software products have expanded over the last 25 years to cover online surveys, tabulations (crosstabs), reporting, online dashboards, CATI, CAPI, paper surveys, online communities, social listening, text analytics, automated reporting, automated coding, text processing and analysis, dashboards and, more recently, AI. As a result of this, there are software packages or platforms that cover one or more of these different tasks. So, should you buy as few systems as possible or the most suitable products for each need? The decisions are getting more challenging as time passes, so let’s look at the pros and cons of using end-to-end software in market research.

Scope of this blog article

This article focuses on the more traditional aspects of market research, covering from data collection to analysis and reporting. Does it make sense to find a system that does everything – or almost everything – and minimise the number of products you use? I will argue that it depends on what you are doing, which might sound as though I am evasive and not providing an answer. However, I will look at each factor that should influence your decision. So, let’s get started.

  1. Learning and expertise

One of the obvious advantages of using an end-to-end system is that everything is in one system. This advantage makes learning easier and, perhaps more importantly, means that a team can become experts or highly competent in using the software. It is likely to mean that a wider group of people know ‘how things work’. With several products covering each part of the process, it is more likely that smaller groups or individuals are responsible and have that knowledge. Having a spread of expertise makes absences, staff leaving, or peaks of work much easier to handle; it is an important consideration.

  2. Data compatibility

Another advantage of an all-in-one system is that data compatibility will not be a problem. You can expect, one would hope, that moving data from the data collection to the analysis and reporting phase is seamless and requires no work on your part. If you use more than one system, you may need to consider how easy it is to move data from one system to another. I have seen data processing teams spend hours or days recoding data to meet the requirements of a second system. This potential problem needs to be checked before implementation as data processing staff can have a mentality of conquering challenges rather than telling management about difficulties.

    a) Common data compatibility problems

Some of the most common problems arise from things that seem routine in market research. The top things to consider are:

• The handling of multiple response questions – there are three or four ways of storing data from such questions. Transferring data to another system may be difficult or even impossible.
• Handling of numeric values – some software may have problems with decimal places or rounding.
• Handling of blank/zero – some software will interpret blank as zero, others will treat it as a missing value.
• Grids/rating scales – different software systems store this data type differently.
• Filters – when questions are filtered, is there a clean way to pass on this information to another system?

    b) Data structures

Repetitive data structures within data are handled quite differently by different software packages. By this, I am referring to hierarchical data or repeated data blocks, often called data loops. For example, you may have a survey with hierarchical data where you interview one doctor and, say, up to five of his patients. Or, you may have repeated blocks of data, where you ask a series of questions for each brand that someone uses or where you collect data for each time slot over several days. How this data is stored may vary significantly from product to product. It’s essential to ensure that any software systems you wish to link together work well if you have this type of data.

    3. Data interchange formats

Two commonly used data interchange formats exist where texts and data can be transferred smoothly from one system to another. The first is Triple-S, which is rubber-stamped by the ASC, an independent organisation. This interchange format is generally a good way to transfer data, although it does have a few limitations, and I have experienced poor quality exports from some software systems which cannot be read back without manual editing. A second option is to use the SPSS SAV file format. This format is the proprietary file structure of a commercial software supplier. However, it has become an industry standard of sorts. This format works well in many cases.

A new kid on the block is TSAPI, which has also gained ASC approval. It uses an API to provide a link between systems. It has only been adopted by a handful of software vendors as of January 2024, but if it is widely adopted in the future, it will be the preferred linkage method. However, checking these ways of linking two or more software systems before purchasing is critical, in my opinion.

  4. Productivity

I believe productivity is one of the most underestimated things in a software purchase decision. Yes, it’s hard to calculate, but as most other business decisions look at revenue and costs, there is no excuse not to try and measure productivity. This is where having a specialist tool can make a difference. If a specialist tool halves the staff time needed, it can soon pay for itself with sufficient usage. I have seen people struggle with software that can perform a required function but where it takes an unreasonable amount of time. If that’s the case, looking for a separate software package makes sense.

   5. Skills match

The level of usage of a particular aspect of the process and the skills available should also significantly affect what software you buy. For example, if your company spends a lot of time preparing online reports, a specialist tool is likely advantageous rather than an end-to-end system that may struggle. Often, more complex software needs more skilled staff, so questioning your available skills and whether training can make your team more able to use more innovative tools is essential.

  6. Complexities

End-to-end software can suffer in two often connected ways. It may have a weak link – for example, its tools for data collection are first class, but its tools for tabulations are highly limited. Secondly, its origins may have been one or two parts of the process, which may mean the other parts are less robust. I know many of the free/low-cost online data collection systems have limited capabilities when it comes to tabulations and reporting. Yes, they can generate a table or a chart, but whether the output is helpful to your processes needs to be questioned. If your work is too complex for an all-in-one package, it doesn’t mean you shouldn’t buy it, but you need to be ready to purchase something more specialist – perhaps if and when your need increases. Budgeting for this in advance is recommended.

  7. Upgrading

Thinking ahead is not easy with software. However, it helps to think ahead and imagine what you might need. It can help you consider what upgrades you might need in the future and how that might fit into how your end-to-end system or product mix works.

  8. Cost

Cost is an issue in any organisation. For those who need to seek budgets, getting a budget to buy an additional product can be difficult when the one currently used ‘does more or less what is needed’. The productivity argument has to be the key to.