When it comes to strategic understanding, data insights are not only all the rave, but also the fundamental foundation to building an evidential intelligence for the organisation to follow. Whilst most brands understand the value of data, many are unsure of the various dimensions of value that it can deliver across their end-to-end operations.
In the 1960 American Western, The Magnificent Seven, a group of seven gunfighters are hired to protect a small village. The seven share traits, but are all different and work best together, so too, when combined, the “Seven Data Dimensions” can protect and empower your brand.
This series will provide an overview of the magnificent seven datasets that most brands have access to and that can be leveraged to gain insight and understanding to design strategies, develop tactics, deliver experiences and drive innovation. Working with the global mTab network and other industry experts this series will pull out examples, and top tips, from across the globe where brands are most effectively harvesting and deploying data.
The Search Is On
Previously, we reviewed social media channels, online reviews, customer feedback, purchase and customer service data streams. The next data source we explore is extensive data from online searches. With the internet’s commercialisation in the mid-1990s, it was quickly understood that there needed to be a universal navigation of the ever-expanding online world. With this, several pioneer search engines were born. These included WebCrawler, Lycos, Yahoo!, Excite and Alta Vista. Then Google arrived, relatively late, in 1997 focusing on search engine optimisation to combat the spam issues the other platforms were suffering from. Microsoft launched its own search technology in 2005, which was rebranded several times until it was finally named Bing in 2009.
Through the continual evolutions, search engines have been a window into internet users’ search behavior and preferences. The Internet’s expansion to mobile and voice searches, has meant the search data’s complexity has increased. This generally makes search data an unwieldy and intimidating data source.
Search data, which in itself is a broad term, including search terms from search engines, searches on firms’ websites and SEO data, has become more manageable as the analysis tools have advanced. This has meant the intelligence drawn from online searches has become a staple of many businesses to add another dimension to customer understanding. Despite this, many brands still view search data as a challenging dataset to manage. However, it is a necessary one to infuse insight into the competitive analysis and marketing performance.
Like customer feedback and social media insight, online search tends to be more subjective and open to individuals’ personal tastes and interests (rather than purely objective and quantitative in nature). Most importantly though, search data is often both incomplete and critically lacks the “why”. The Google Flu Trends tool is a good example of this and well worth a read:
https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/
However, Search Data, remains a valuable data source and can be quantified, measured and analysed for trends. Given this, whilst, reviewing search data often requires a higher level of interpretation and analysis given the unstructured and subjectivity of the information, when done well, the value of this information is widely embraced.
As Justine Clements, Consumer Insights Manager for Samsung Australia explains,
“Search is the point of entry for most to the online activities for consumers and makes the process of finding what they are looking for manageable. Given this, there is a wealth of intelligence within consumer search activities that can advise the decisions and developments of a company. It’s important to note that there are different dimensions of search.”
Having a solid, consistent understanding of the dimensions of customer attitudes and behaviours as well as engagements and issues is important in not only identifying and fortifying the points of strength across specific products, services, channels and promotions, but, perhaps more importantly, pinpointing and addressing the points of weakness against competitors.
There tends to be significant value in ethnographic insight from sources like social media and online reviews. In the same manner, there is tremendous value in understanding what consumers are seeking in their online searches to help provide further insight on their path to purchase. Whether they are seeking product comparisons, analysing customer reviews or comparing prices all have important implications in how businesses position their products.
Whilst search data is unstructured by its nature, it also provides the important ethnographic aspect, that is “observing” consumer activities free of solicitation.
Controlling the Complexity
The complexity of search data is not unlike that of customer service data which we reviewed in the last article of this series. Historically the unstructured nature demanded advanced tools to manage the information and extract the insight but with the advent of tools such as Google Trends and Google Analytics, this has become far easier. Like ethnographic social media content or solicited customer feedback or observational customer techniques, search data tends to require technology use to analyse, manage and respond to the constant flow of information. It also generally requires employees to analyse and translate trends into actionable responses by the appropriate teams.
The good news is that although it can be complex in nature, as mentioned, there are increasingly standardised mainstream tools available, so identifying appropriate solutions is not that difficult or risky.
According to Mark Ursell, CEO of MindMover,
“The Internet has consistently been built on a search foundation since its commercialization in the early 1990s. As it has exponentially grown, search has become even more critical and sophisticated in order to sort through the mass of information to find relevant intelligence. Along with it are tools that have helped to understand the usage patterns and trends of search on both a tactical and strategic level. Search data can provide incredible insight on the preferences and important elements to people. It can deliver understanding across an array of levels to drive expansion and innovation. However, there can be an inherent bias within search algorithms, which make using the data more challenging. The key is to consider the data stream and cross reference it with other data streams while building in primary research to understand why these behaviours are happening.”
Intelligence Boundaries
The expansion of search is moved well beyond the Internet. There are now search components within websites, apps and even documents and whitepapers.
As Clements explains,
“While many brands focus heavily on the search giants like Google, which provide a wealth of insight, it’s also critical to understand how individuals are using the search functionality on the company’s website. Looking at consumer behaviour through a behavioural economics lens can greatly streamline the customer experience from brand through to product, particularly in terms of site navigation, portfolio and product merchandising and promotional activity, all of which can deliver enhanced experience to shorten the path to purchase. Our purpose is to help consumers make decisions more quickly, with more confidence and ultimately deliver better product satisfaction and hence loyalty.”
Analysing the data within these proprietary search engines is even more critical, since they deliver insight directly related to how consumers navigate and consume a company’s information and content, and ultimately how they purchase. This provides a wealth of insight in how to promote, position, merchandise and market.
Beyond this, proprietary search information can identify issues related to objections, information needs and successes and failures with the path to purchase. In other words, similar to how purchase data objectively displays the performance of products, proprietary search data can provide both tactical (short term) and strategic (long term) views into how online channels are performing including websites and apps.
The challenge is sorting through the complex, unstructured nature of this data and the fact that it typically sits across a spectrum of systems. It must always be remembered that there is a plethora of search engines in use, Google, whilst a dominant player, is not the entire market/whole data source but one subset and when analysing data from each search engine, the practitioner must be mindful of all the usual methodological pitfalls (e.g. sample bias).
Challenging Limitations
Given all the benefits that internal and external search data have, it’s important for a brand to review and track it consistently to measure customer’s online behavior. Unlike quantitative purchase data and qualitative customer service intelligence, search data can have an inherent misunderstanding as to the intent of the customer’s searching. They can be searching on behalf of others or are just curious about a product or had a mis-click, which can be challenging to filter out. However, despite this, it can still be an effective data source.
It is the nature of search information that tends to frustrate marketing and product teams.
As Marcin Godlewski, Head of Product for mTab, explained,
“Navigating the online world still heavily relies on search engines, which means there is a treasure trove of information and intelligence on your own brands and products as well your markets, industries and competitors. If the goal is not only getting ad-hoc findings, but rather identifying trends or measuring performance, repeatability and comparability of search instances becomes key. So, maintaining consistent management of both proprietary and third-party search data is critical, but has to be done carefully.”
Whilst some leading companies place search intelligence at the forefront to help set their strategies and promotions, particularly around marketing and ad buys, there are other brands that discount its value given the subjective, complex nature of the data.
Tracking the Trends
It’s important to note that spikes in search data can relate to elements like press, promotions, offers, product launches and even controversies. This can result in factors that can impact search data’s volume and direction of search data. This includes, but not be limited to ad buys, news articles or product launches. It is important to anticipate and note how these types of events and factors will impact the search data volumes and tracked terms.
Search engine optimisation related to content and online messaging is also an important dimension to pay attention to in order to drive search results. This is a direct factor in search conversion but also adds to the daunting complexity of the realm of search.
Getting Started
The first step in getting a handle on search data streams is to establish the internal and external search sources the business wants to use to track. Centralising these varying data sources will help to ‘blend’ and understand the context behind search data.
Like with other datasets, search data should have an identified centralised owner of the information. All too often the ownership of search data becomes a topic for political infighting with different groups (typically marketing, sales and e-commerce) all competing for ownership. The reality is that because search data is often more incomplete than other data sets there needs to be even more collaboration to understand this data set than some of the previous data dimensions explored. Whoever ends up “owning” this data dimension, the ownership group should set up a process and frequency of reviewing the data reports to analyse and understand trends in terms of timing, frequency, volume and seasonality of purchases.
As the data is reviewed, there should be a focus on identifying input and result trends to identify and isolate issues and opportunities. From here, there should be point owners across every team, including Operations, Sales, Product and Technology, given the array of feedback that can be expected which can impact any of these, or other teams in the organisation.
When a trend or spike emerges pointing to an issue or opportunity, these individuals should work congruently to quickly determine the scope of the issue or opportunity, the impact on the company and the response in terms of operation and communication. There should also be an understanding of the potential root cause of the change, whether a promotion, news story, announcement, product launch or some other factor, in order to understand what may be ‘moving the needle’ in order to address or replicate it.
With each data source, the key is to strategically set a plan in place to identify, collect, analyse and democratise the intelligence in the most effective manner possible. There also must be ownership of decisions in terms of assessment, response and reaction. From there, it becomes a matter of creating a habit of returning to the data to identify and understand shifts and evolutions in order to design strategies, develop tactics, deliver experiences and drive innovation.
Whilst daunting and challenging in scope and nature, search data is just one of these ‘Magnificent Seven’ datasets that can empower your brand, and although it is among the most complex, it also can deliver significant value. Stay tuned in this series as we continue the review of these dimensions of data to help guide understanding of the holistic health of a brand.
