The Digital Transformation of Companies
Updated: Jun 4
Customer experience is at the heart of corporate strategy in the digital age. Companies need an ongoing digital transformation to achieve the sort of experience that customers seek out again and again. This article shows the several stages of digitalisation – including how digital and analogue companies differ – and explains how digitalisation impacts the customer experience.
Digitalisation may be a thoroughly modern phenomenon, but it hinges on a classic concept of the purchase processes: Each and every customer goes through different touchpoints, with various companies, during his or her journey towards becoming a customer.
Let’s start with the idea that every customer has a general problem or question that requires a solution. In this age of constant, data-driven solicitation by companies for customers, a purchase process often begins with entertainment: A problem or desire is created – for example, the desire for a sportier and safer car – even if the customer finds their current car entirely suitable. Once a problem or desire is recognised (or, as is often the case, created), customers research and compare offers until a transaction occurs. Generally, companies that are most able to solve the customers’ problems at a good price achieve the best results, and are able to win the largest share of the market. Here the paths of traditional and digital companies diverge from one another in their approach to customers. Acting two moves ahead This situation for a company can be compared to that of a chess player. A good player will gather information, consider moves, calculate probabilities and make the decisions for their next moves. But what happens when a good player – even the best player – is playing a computer? If we assume that a computer can perform three dimensions (information collection, information processing and decision-making based on information) significantly faster than a person can, then it is inevitable that a person would lose to a computer in a head-to-head match. Indeed, computers have had the upper hand on the world’s best human chess players for more than a decade. If we compare this example with the current economic situation, then companies using the fruits of information and communications technologies are better off than traditional companies. The jump from chess player to programmer – the one who builds the chess-playing computer – is analogous to the transformation of a traditional company into a digital company. Companies that are digital from the start do not learn chess themselves in order to play against others. They start right away building a chess computer. Not long ago, in 2013, the German Chancellor, Angela Merkel, stated, “The Internet is new territory for everyone.” Here in Germany, we are scurrying to simply develop websites for our billion-dollar companies. Meanwhile, in Silicon Valley, cybernetic super companies are being built not only to understand and solve the problems of their customers, but also to calculate each step of the market’s own value creation chains to develop and play out counter strategies. Fundamentally, the strength of a system increases with the depth of information that it can collect: The more information that can be collected, the better the predictions on future moves of the other players will be. The digital transformation On the one hand, there are some established players such as Zalando and Amazon that were digital from Day 1. On the other hand, there are companies such as Otto and Walmart that, as multi-channel retailers, are coming from the brick and mortar analogue world and developing towards becoming digital companies. The fundamental elements of this digital transformation observation are valid for retail as well as companies in other industries such as financial services, media and publishing, automotive, B2B, industry, telecommunications, travel, service and so on. When the foundations of the structure of digital and analogue companies are compared, they differ from one another primarily in the embedding of processes for data collection and data processing, as well as the technologies selected.
This model is, in addition to the production of real goods, also applicable to the transformation of data into the decision-making process for marketing and sales activities. This data can come from external or internal sources. External sources are self-collected data or data collected from other sources in the market such as from experiments, surveys or censuses. Internal sources are, for example, customer relationship management (CRM) systems, supply chain management (SCM), enterprise resource planning (ERP) and payment systems. As part of a modern-day digital transformation, the data is analysed and then provided to the parties who will act upon the data, such as Marketing and Sales departments, which then produce the output.
In an analogue structure – especially companies which originated in traditional brick and mortar retail – the market research department takes care of the data transformation. The collection of external data from experiments, surveys or censuses is based primarily on random samples of less than 10% of the entire group. The decision preparation is thus based on a statistical estimate for the entire group. Big data transformation Consider for a moment the readership for a parenting magazine. It is known what the circulation of the magazine is, how many issues are sold per region and the purchasing power of those regions. Via surveys or other methods, a deeper analysis into factors such as the gender breakdown of the readers and the household income of addresses that subscribe makes it possible to execute data-driven decisions about product advertisements in the magazine. Meanwhile, the Internet’s infinite data collection possibilities enable companies to collect 100% of the data from websites, mobile applications and online marketing channels. In other words, with the Internet, you do not work with random samples. You work with mountains of data. The analogue company is usually not prepared for the challenge of so much data. They are overwhelmed by the sheer quantity of data, and also by the speed with which decisions must be made and implemented. For an analogue company, a data transformation process consisting of input, transformation and output can take from several weeks to many months. In comparison, digital companies are positioned to react within a few days, a few hours or even in real time.
If we take this in the context of our analogy of the chess player and chess computer, the analogue company is the chess player and the digital company is the chess computer. The difference in the company structure appears to be marginal. However, this structural difference – to say nothing of the results – is enormous. In a digital company structure, market research or a similar department is not responsible for the process of transforming data. Instead, the Business Intelligence department is responsible. Furthermore, the digital company will almost invariably set up a data warehouse in this processing step. With the combination of Business Intelligence and data warehouse, the actual data analysis for decision-making is no longer performed by people with the help of technologies such as Excel and SPSS, but is programmed so that, ideally, the analysis is automated and available immediately. The required processes on the operative IT level are, amongst others, Extract, Transform and Load, or ETL. The ETL process is the ability of the data warehouse to save homogenous and heterogeneous data sources together and, depending on the requirements, to make all data available. The art of this is to take the mass of data from all sources such as the website, CRM system and market research, and provide analyses that make decision-making possible without sinking into data chaos. The focus here is not on a single enquiry, but on the complete control of the data transformation process. The challenges of this are, as mentioned, the mass of data and the speed with which this data must be applied to decision-making. Cultivating a data-driven mindset and, importantly, a data-driven infrastructure makes it possible to establish processes within the company to ensure, for example, that hypotheses on website improvements are quickly tested, and that the winning variations are immediately set live so that the next improvements can be executed. With this method, checkout processes or landing pages for online shops can be optimised. We can imagine many of these tests going live per month for a constant flow of website improvements. With a traditional company structure, this is rarely (if ever) possible. At this point, it is necessary to mention that in order to keep up with the speed of data transformation, further processes must be developed while digitising the company. Agile management methods, for instance, are a popular instrument that can be applied to Marketing, IT and much more. Many companies that do not have the luxury to truly leverage digitalisation from the beginning now have a large challenge to adapt this approach and embed it in a company structure that may have already existed for years or decades. In order to optimise the data transformation and related ETL processes in the future, companies need to decide how the topic is to be addressed in the top level of the organisation. Even with limited resources, it is vital to create a Business Intelligence department that sets up the data warehouse and coordinates the requirements from marketing, web analytics and IT.
A digital company culture Currently, the Data Management Platform (DMP) is viewed as the next step in the digital transformation of companies. The challenge here is that there is no common understanding of how it is different from a data warehouse in affecting the communication process. The difference can be explained by thinking of a potential customer driving down the highway looking for a restaurant. Perhaps, at this moment, that customer really wants Mediterranean food. If a nearby Mediterranean restaurant were armed with this knowledge, it could display optimised advertising on the nearest billboard. This Meditaerranean restaurant would win the customer because they put up the sign and spoke directly to the customer’s needs. And they did it in an instant. But that does not mean that the restaurant specialises exclusively in Mediterranen food: It could be that the restaurant actually offers just basic salads, including a Mediterranean salad. They can offer something else to a different customer with different needs – for example, a sign for a low-carb lunch. The problems of both customers are solved. This sort of real-time personalisation enables digital companies to break through the value creation chains of their competitors by leveraging insights into purchase and decision-making processes. For those companies with a digital structure, the next dangerous and largely unrealised competitive advantage is the acquisition of information from competitors about customers in the market, and about the competitors themselves. No doubt, the acquisition of such information is already happening. The Open Automotive Alliance, for example, is a platform created by Google in order to develop a standard for the digitalisation of automobiles on the basis of the Android operating system. This enables automakers to offer customers a better experience via computer boards, navigation and music systems running on Android. In this case, the creator, Google, sits in Mountain View, California, and has the possibility to collect data from end customers around the globe. Over the long term, it is thus possible to communicate with the end customer and dramatically disrupt important value chains. A logical next step would be the possibility to further develop navigation systems in self-driving cars for advertising in local markets: If a driver is interested in Mediterranean food, the navigation system knows this desire and has the possibility to display a sign for the highest bidder. DMP as a transformation engine Let’s now concentrate on the properties of a DMP in relation to the digital transformation of a company in the first stage consisting of Business Intelligence and data warehouse. We will not concern ourselves with a complete definition of a DMP, but instead focus on the role of the DMP in relation to the data warehouse. By far, the most important property of a DMP is speed. While an analogue company may take weeks or months to execute the data transformation process, and a company with a traditional Business Intelligence and data warehouse structure may take just days or hours, the focus of a DMP is to achieve results in real-time. At this point, the decision to implement a real-time data transformation process is potentially a headache. This real-time aspect was often not stated in the original requirements upon setting up a data warehouse; even digital companies are not always well-positioned to develop real-time DMP capabilities with the necessary efforts. That said, if a company wants to implement a real-time data transformation process with a high-level of data quality, it is difficult to avoid a DMP. Real-time results The real-time requirement is a consequence of the purchase decision process discussed at the outset.
If we look at the individual purchase decision processes, individual customers could have various touchpoints with the company. To make this thought experiment simpler, let us for now consider only people who encountered products via online marketing, for example, display ads, onsite recommendations or a newsletter.
It is possible at every touchpoint to collect information and offer the customer a solution that matches their needs. In order to provide this, a company must be able to deliver, in real-time, the solution to the problem of precisely the customer in question. As fast as possible, the most recent information needs to be considered together with the entire data history in order to offer the best solution to the customer. Let us take for example an already known customer: female; 32 years old; regularly buys pants (instead of dresses) and sneakers (instead of other types of shoes); and receives a newsletter with the newest collection of the season. With this information at hand, it is possible with a DMP to use the newsletter to offer the relevant clothing articles in the newest collection, and to show these articles in other touchpoints – such as display ads or on the landing page of the web shop. With a data warehouse, the shopper has come and gone before an ounce of data can be put to use. DMPs enable further mixing-and-matching of campaigns. So in addition to targeting likely buyers with exactly what they have enjoyed in the past, you can engage customers by offering articles from the new collection that do not necessarily fit their previous buying history. The idea here would be to figure out how to maintain or win back non-loyal customers. The decisive advantage of a DMP is that it is Software as a Service (SaaS): The actual data transformation occurs automatically via the DMP and can be configured by the Marketing department. Business Intelligence develops and maintains the basis of the DMP, which is a decisive factor in the digital transformation of a company, but the actual transformation takes place automatically based on pre-defined rules. The transformative power of DMPs rests on their ability to segment customers based on the most diverse set of available dimensions; enhance these segments via predictions; and then deliver individualised ads or content via various channels to the customer. Delivering display ads based on customer behaviour has been possible with various tools for some time now, but in a limited capacity compared to what is possible with a DMP. In general, with classic retargeting, the rules for displaying products are not transparent and cannot be configured. A lot of the time, such retargeting technology simply displays last seen products. Furthermore, data leakage is a serious concern with classic retargeting: In order for retargeting tools to work, third-party cookies are used to collect data. If we look at various websites of well-known companies in the market, it is not uncommon to see 15 or more tools integrated into the site, each one using third-party cookies to collect user data. With this information, the 15 companies are better positioned to understand the purchase decision processes of customers than the actual company who is paying these 15 companies for the services. Thus, these third party companies are better positioned in the long-term to understand the problems of the customer and offer an appropriate solution, whether it be from your product offering or from the product offering of another player in the market. With this process, potential benefits of digitalisation for your company can be leaking from your company.
Data merging platform One of the biggest current challenges for companies in any stage of digitalisation is merging the online and offline worlds, which is happening concurrently in real-time with the data transformation process. A company with an established DMP and the successful collection of offline data, as well as the potential to perform personalised offline marketing activities, is truly in the third stage of digital transformation.
In addition to online contacts such as display ads and websites, the customer has many more touchpoints with a company. That is why it will be such a competitive advantage to integrate offline contact points from the customer journey into the processes of understanding the customer’s problem, which improves the communication between the company and the customer, as well as the quality of the solution offered to the customer. Offline is used here to describe sources and touchpoints that historically do not come from online and until now have seldom been integrated. Once a company has been successfully digitised, the offline sources and touchpoints should be digitised and the system should be expanded. Once these offline touchpoints have been integrated into the data transformation process of a company, they cease to be considered “offline” in a literal sense of the term, but are mentioned in the diagram as “offline” for the purpose of simplification. Consider looking for a new tablet. A customer may often follow their online research with a visit to a traditional store to take an up-close look. At this step, a preference for particular brands can be noted or even used when interacting with the customer. If the customer has shown more interest for the tablet, the communication can be continued via a newsletter or by showing reviews and reports about the product on the homepage. The Internet of Things, such as cars, living rooms and wearables, will mean that many offline touchpoints will be digitised by default and made available to companies. At the beginning, this data will be for the manufacturers themselves, but in the future it is possible to imagine that this data will be made available for other companies via a market. The topic of potential data leakage becomes even more complex as there will be a relationship between the company responsible for the primary thing (for example, a car) and the manufacturer(s) of components of the things (navigation board, music system, etc.,) within the car – not to mention the owner of the operating system upon which these components run (Android, iOS, etc.). Thus, it is important to examine which standardised platforms for digitising offline channels should be connected to your own system, and which ones should not. This digitalisation of companies reflects Webtrekk’s vision to enable their customers with a Software as a Service approach to become thoroughly, wholly digital third stage while protecting the collected data. Your transformation As indicated in the previous section, we recommend starting digitalisation as high in the organisation of the company as possible: A bottom-up approach has a much higher chance of creating conflicts between departments with different goals. For this reason, the success relies on digitalisation being realised and driven by the top decision-makers in the company. Successfully digitising companies often establish a Chief Digital Officer as the main or side function of a COO, who is the main actor in implementing the digitalisation across departments and communicating the sustainable vision in the company.
Another very important first step in the evolution towards a digital company is the integration of a web analytics system for analysing website, mobile, apps and online marketing channels. The best choice is to integrate this element in the Marketing department, because this department is often also responsible, for example, for the online shop and is already operating as a profit centre in the company. Web analytics is often placed under Finance or another related department. This, however, invites tension between controlling the finances on the one hand and optimising the business based on a data transformation process on the other hand. In some cases, web analytics is part of the IT department, which, just as when web analytics is in the Finance department, leads to conflicts of interests such as in resource allocation. Both departments are often focused on reducing costs, but should in the long-term be run as profit centres largely dependent on the results of the data transformation. Let us consider the example of a multi-channel retailer, and the optimisation of their shop for tablets and smartphones. Marketing is dependent on the constant involvement of the IT department to run the various insight-generating tests for web analytics. For this, it is critical that a decision be made on the required process. With the implementation of agile methods, this is often derived from a project management approach with which the requirements on the IT team to implement new ideas are coordinated with the available resources.
After a Web Analytics department is integrated, a next step is often setting up a data warehouse. In this constellation, it is recommended to place the data warehouse setup with the IT department. While there will be some friction points, it is seldom the case that the Marketing department has the know-how for setting up and managing a data warehouse.
Following a digital blueprint Even with limited resources, it is definitely recommended to directly create a Business Intelligence department, which sets up the data warehouse and coordinates the requirements from Marketing, Web Analytics and IT. From the beginning, this BI department should gather requirements from the profit centres, such as Marketing, translating them into the necessary ETL processes and developing the data warehouse on that basis.
In the long term, the Business Intelligence department – along with the management of the data transformation process – should be on the same level in the organisational hierarchy as the other fundamental departments. In today’s highly digitised world, the quality of company’s data is a determining factor for the performance of the entire company-. In general, a functional organisational structure – a complete centralisation of Business Intelligence – should occur from the beginning. Companies that have made strides towards true digitalisation take the structure topic even further. A frequently seen variation is that the company structure itself is based on the individual elements of the purchasing decision process. The trends vary and can often be quite specific to an individual company or industry, but the common thread among successful companies is an appreciation for data, and an emphasis on using data to drive decision-making. In order for digitalisation to become a part of the overall company strategy, you should plan digital initiatives with the leaders of various departments, which are then implemented and measured for success based on clearly defined KPIs. In order to get a feeling for the status quo, it is helpful for the company to pose questions to itself. Based on the resulting answers, the necessary initiatives can be defined. These questions could be, for example:
What does the customer journey and decision-making process of my customers look like?
How does my company’s value creation chain function?
At which points in my customers’ decision-making process can customers decide not to go with me?
At which points in my company’s value creation chain is data given to third parties?
Important first initiatives are often the establishment and consolidation of a customer-focused mindset; the installation of goal-driven data collection processes at the most important touchpoints; and constantly increasing the speed of internal communication processes of the data transformation process. Further initiatives are the identification of needed digital skills and the analysis of the improvement potential of the company for these skills. Next, HR and the team(s) responsible for digitalisation should work together to develop a plan to close these gaps. How to build it The analysis and selection of the system landscape are largely responsible for the speed and synergy effects of the data transformation process. In other words, they are vital steps. We recommend that the following criteria in particular are considered:
Are the end customer (user) and their decision-making process in the centre of the system solution at hand, so that this system is a fit for the future direction of the digital company?
Can data silos be broken down by integrating as many parts of the solution as possible in various departments? This creates a cross-departmental architecture that enables the decision-making process of the users to be analysed, and coherent communications with the customer to occur.
Are the necessary requirements of real-time collection of offline touchpoints, as well as communication with customers at offline touchpoints, accounted for in the data transformation process?
Is it possible to protect the relevant user-centric and decision-making data, so that third parties cannot use them for their purposes?
Does the solution scale with increasing digitalisation and the business model of my company?
Is it possible to easily display, for top executives, the most important success criteria of the company with regards to digitalisation?
Furthermore, we recommend, in addition to a CDO, that other change agents are integrated, under the responsibility of the CDO, in the individual departments to support the cross-departmental digitalisation of the company both functionally and in terms of promoting the sustainability of the digitalisation. Where and when possible, these change agents should be empowered with decision-making powers via the CDO, COO or CEO for setting up new technologies in order to support the transformation and to avoid delays caused by slow decision-making processes. Digital is a requirement, not a gamble In general, people in companies are sceptical of change. But they are the most important factor in the success of the digitalisation of the company. Thus, it is important to include people in the early phases, in order to minimise reservations about embarking on this digital journey. Simply put, many people worry about losing their job due to the digital revolution and transformations to company structure. The scepticism is not entirely misplaced. This can result in fear of cannibalisation effects, an increased “bunkered silo” mentality and potentially less willingness to work in teams. These fears stand in the way of digitalisation and should not be ignored in this process. To deal with these potential obstacles, the previously mentioned initiatives can help, such as personal development plans, identification of digital skills and a 360-degree feedback process. A more decisive factor is unified, open and sustainable communication across the management level of the company showing the new opportunities, jobs and growth created by the transformation. Additionally, the company culture requires special attention. To a certain point, the culture and people in a company need to be digitised, so that the entire company can react at the necessary speed via digitalisation. With the implementation and development of the data transformation process, the rate of change from inside and outside the company from customers and the market increases. This requires a company culture that is open to this constant change so that reactions in the form of faster decision-making can take place. This openness to new ideas and approaches must be formalised: The structure of the digital company, in contrast to yesterday’s non-digital company, needs to be in a constant state of change. Connected to this openness, a culture of embracing failure should be cultivated. Instead of fearing mistakes, it is important to encourage learning from mistakes and appreciating the productive potential of errors. With this change, the innovation possibilities of the company increase, as does the speed at which reactions can be made. As is often the case with transformation and change management, a lot depends on the leadership of the company. This shows itself in small things such as top management using elements of the digital age, which could be simple items such as using a tablet, wearable or self-driving car of the future. Only when the top management and their teams act as examples of the transformation can the transformation be projected onto the company.
This article was first published by WebTrekk. Permission to use has been granted by the publisher.