Apr 26, 2022 8:03:00 AM
Consultant, Full Stack Marketing
This blog is part of our book: Data-driven digital commerce – turn data into revenue. Download a copy of the book in the green box to the left or at the end of this blog.
Some companies store immense amounts of data but don’t make much use of it; others have various data sources but don’t actively collect, analyse or utilise any of it. Both of these situations are bad for business – but there’s a way out.
Pretty much every company in today’s world operates digitally, which means they have plentiful sources for multiple kinds of data at their disposal. The level of maturity in terms of the collection and utilisation of data varies a lot in different industries and markets, leading to different types of challenges. Among Nordic companies that are not effectively taking advantage of the available data-related opportunities, two common scenarios emerge clearly. Either the companies have data in large quantities but this data isn’t connected, or there isn’t enough data simply because it isn’t being collected.
Both issues have the same origin – the value of data isn’t recognised. Either the business has not found enough business cases to connect their data or the data collection procedures have not been set up, making it impossible to drive growth activities through data. Both scenarios are keeping these companies from finding opportunities for new growth and enhancing their customer experience.
Even though these scenarios might come across as mutually exclusive, over time many companies will face both situations on different levels. Even if you are working in an established business setting and you have a long history of collecting data, things can change. Expansion into new markets and disruptions in the markets that you already operate in may lead to much of your historical data and existing data processes becoming redundant.
It is valuable to critically analyse what stage your company is at on your data journey. We will attempt to help you do this by describing the typical challenges and characteristics of the above two scenarios and explaining how you might overcome them.
In this scenario, the business either hasn’t collected and stored any data at all or it hasn’t defined what to collect, how, where and, most importantly, why.
Collecting and analysing data enables better and more personalised customer experiences. A holistic overview of customer experience – also known as Customer 360 – helps to understand a customer’s needs and possible hiccups in the relationship, as well as predicting its future direction. Without connected data as the basis for new customer touchpoints, every encounter with a customer starts from zero. Think about a customer being transferred from one department to another, and then on to another – each time having to explain their history all over again.
Data also allows for better targeting of products and services, enables cross-selling and is important for customer retention. What might happen if the sales department isn’t aware of a customer’s unresolved complaint, or the marketing department approaches them with an offer they have already accepted? When there’s a holistic view of the customer’s historical interactions with the business as a whole, it’s easier to spot when a customer journey is taking a wrong turn or the relationship is beginning to fade.
If functions such as sales, marketing and customer service are already well aligned, the willingness to collect data across different customer touchpoints might exist within the organisation, but the practice hasn’t been successfully implemented across it. It’s possible that there is resistance to change: for example, if a salesperson is asked to collect information regarding customers without an explanation of why this is important and beneficial, they will probably see it as an unnecessary increase in their workload. Consequently, when the salespersons' schedules get busy, their view of the collection as “data for the sake of data” will not help keep the CRM up to date.
In a company with no significant data collection practices, most likely there are no successful business results from data-related tests to be shared with the people who could help collect the data. For companies with no established data collection, it may be good to start with tests where data can be utilised right after collection so that potential positive results can be uncovered earlier on to boost the motivation for larger-scale data collection, operations and analysis.
This scenario can also apply to new companies that are yet to establish data collection procedures and data architecture. However, in their case the change might be simpler to implement – there are no old habits to get rid of. By not having a lot of valuable data in potentially incompatible formats, the business may have more doors open than those that already have data but aren’t using it as they should be.
To get started with data collection, first identify the customer data sources with the most potential – an online store, a website, sales contacts or possibly customer communications – and investigate the changes that are required to your technical architecture, analytical capabilities and processes to collect it.
For example, does the utilisation of ecommerce data in other functions require a complete change of the data model, and possibly the online store platform itself, or is it already possible to connect that data with other sources on a centralised customer data platform (CDP)? If you do not have legacy systems and processes weighing you down, don’t operate as if you do. Don’t try to establish a technically perfect solution from scratch, but start by testing individual business cases and use the results of growth experiments to back up your investments in data.
Even with an open playing field, keeping scalability in mind and having a holistic view of different experiments conducted by different business units will give valuable insights that will help you grow into a unified and data-driven business instead of what is presented in scenario two.
Typically, companies that have operated in digital channels and utilised digital solutions for longer have already accumulated a lot of customer data.
Nevertheless, the data competence may have evolved unevenly and data analysis and operations may be fractured without shared goals. This is often the case with traditional organisations where business units and functions are siloed, and they might even use different data systems that are incompatible. They might have collected data – a lot of it, or even too much – but it resides in silos, is often unstructured and can be difficult or even technically impossible to bring together due to compatibility issues. The sales department may have information that could be used for marketing and vice versa, but there’s no communication between the two.
The problem often stems from limited capabilities when it comes to finding shared use cases; although the data has been collected, no one has taken ownership over its utilisation and it doesn’t travel between units or functions. Poorly organised data management easily causes valuable information to get buried under a mass of irrelevant data. Is the data relevant to begin with, and is it collected in a standardised format that can be analysed efficiently?
In a mature data-collecting organisation, the data that is most valuable to any given function often resides within a specific function of the company. Oftentimes, one department might collect data using structures and classifications that aren’t relevant to another; for example, there might be plenty of detailed data about customers’ behaviour in a customer portal but it is not structured in a way that could be visualised for the sales or customer service functions.
Simply passing raw data from one part of the organisation to another is not enough. Instead, the data needs to be aggregated into meaningful information and embedded almost seamlessly into the existing context and processes of a given function. No salesperson wants to spend their time analysing customers' clickstreams, but most would want to know if their key accounts have suddenly dropped engagements in the order portal. In this case, the challenges that need to be tackled include architecture and processes.
If the data architecture enables the utilisation of data, the solution can be setting up shared goals and continuous cooperation between different business units. This requires active engagement on all levels of the business and a unification of all data-related planning activities, from individual sprints to roadmap and strategy.
More often than not, the data and system architecture is also siloed. There may be a need to invest in a centralised data platform that unifies data from different sources or, for example, builds integrations between legacy systems and modern tools to enable a quicker impact. Even if there is a need for a larger unification of the data architecture, it’s already good to start testing business cases to support growth and customer experience during implementation and use the insights from those results to continuously drive data capability development.
If the organisation is waiting for the perfect opportunity to utilise data in business cases in a way that optimises every single customer’s experience, the return on investment will never materialise.
In both of the scenarios outlined above, the target situation is the same: an organisational culture in which everyone understands what kind of data is needed and what it is used for. From an individual employee's perspective, data should not be seen as something about which individuals should send reports to their superiors or a box to be ticked on a to-do list; instead, it should focus on adding value for the customer and being important to all functions, customer facing or not.
To drive this kind of culture, the key people in the organisation need to actively showcase their successful data utilisation use cases across business functions.
Many of the issues that may be encountered can be handled with the help of automation, which also ensures that the data is standardised and reduces the risk of human error. However, manual data collection can’t be fully replaced; what is really needed is cultural change so that everyone involved takes ownership of data collection, analysis and usage. This takes plenty of internal communication and explanation of why data collection is not just an extra chore but a competitive factor and a quintessential part of strategic planning.
If data is to be used efficiently, building organisational bridges between different functions is necessary. Starting from business-specific key performance indicators (KPIs) and gathering multidisciplinary teams to discover and define use cases is an investment that should lead to concrete findings on the applications of data. Even that will take a lot of trial and error. In the long run, not exploring and experimenting with the data you have might turn out to be the most expensive trial of all.
This blog post is part of the handbook Data-driven digital commerce – turn data into revenue. The aim of this handbook is to help a sales, marketing, IT or ecommerce leader to formulate an overall understanding of the critical themes for turning data into concrete business impact. The book includes interviews with global thought leaders on the topic as well as industry experts from companies including Supermetrics, Singular Society and Kesko.
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