The time has come to break paradigms; digital transformation is not only about transforming the business based on technological ecosystems. Today’s customers have less patience and less time. They are looking for experiences that adapt to their needs and expectations, expectations that are individual and constantly changing.
Companies today are making a mistake, they are adapting the physical business to the digital realm, without taking into account that they can create different experiences, with the same purposes, but taking full advantage of everything that a digital native ecosystem can offer. We believe that experiences should be digital native and not forced to be converted from physical to digital experiences with the same attributes and characteristics. On the other hand we are perceiving in the environment of change, the massive error of companies to create generic experiences which are generating a desertion by the customer. As we mentioned in the introduction, today the customer has more power, more options, more alternatives to access at the click of a button. According to Rubén García, CEO of OMNI.PRO “Undifferentiated experiences become a commodity, because the customer does not perceive anything different”.
Companies are putting more effort into spending more time and resources on topics such as data mining, information enlistment, extraction, but not necessarily to decision making. One of the biggest mistakes we are seeing around analytics is that companies are only looking at past data and not focusing on the future. We are finding that more and more businesses are making decisions on their subjective perception and making changes to flows without really understanding what is going on in the business. They make decisions on stale data, making poorly agile and ill-advised decisions about the type of experience that would allow them to offer variety and unique experiences to each customer.
In this way, the subjectivity at the moment of making decisions generates challenges around the fact that there is no capacity for experimentation, generating value agilely in the face of the customer’s experience. Experimentation is necessary to iterate, create prototypes and hypothesis scenarios in relation to experience, promotions and formats in order to have control. This is an input to make decisions and to meet new users. That is why we talk about one of the most relevant challenges of companies in the process of transformation, the deficiency to include the data strategy in the different teams of the company. It is necessary to develop methodologies where we deal with iterating and creating hypothesis scenarios in relation to the experience. Experimentation through promotions, for example, leads us to have control over the behavior of a specific group of customers, which becomes an input, not only to make decisions but also for the creation of new profiles, to have a practical guide to the most effective strategies.
On the other hand, another big challenge is to bring together in real time the part of analyzing customer journey data and transforming it automatically into an automated experience. Many retailers and businesses are faced with having to offer experiences that are truly omnichannel, and they can’t be analyzing and aggregating information into a customer profile in order to bring in the interactions they have in other channels. Today the customer experiences the brand in many places and shows interests and behaviors that are important to understand what he wants, how he wants it, when he wants it and where to be able to analyze that data, capture it, grind it and visualize it together with the behavior he has in digital channels, becoming much more valuable, because you no longer only have the customer view in digital channels, if not the End to End customer journey.
In conclusion, a digital transformation process is not exclusively the development of technology. Technology is usually 20% of the effort that has to be dedicated and the remaining 80% has to be oriented to the transformation and evolution of the culture of the business and organizational vision. These points that we have touched on are the ones that are sought to spin the experiences, and that are then activatable in an experience that is differential for the business strategy. Next, we will solve some of the doubts and questions of our customers, asked to Eugene Balayan Analytics Manager of Falabella.com and his perspective around omnichannel analytics. In the next part of this Blog, we will continue to share our vision on trends from different points of view, to improve the customer experience from the indicators and business strategies in any industry, expect it very soon.
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1. Question by Helbert: As long as the customer is authenticated on the site, automatic personalized recommendations can be made. How has Falabella done to generate recommendations when it is not known who the user is (anonymous user)?
A good way would be to cross-reference the purchase order code with the user’s RUT/DNI from the company’s transactional databases. This way, knowing the identity of the user – you can do personalized marketing, or targeting. Note that in this case Analytics or Personalization tools are not involved. However, additional insights can be gained by cross-referencing the purchase order code with other data. It is important to note that at no time can private, identifiable user information end up on the dialing or Analytics solution provider’s servers.
2. Question by Luis Chourio: What is the most effective way or methodology to change the culture and the way different areas of the organization work and adapt to technological changes?
The necessary condition of any communication is that all participants speak the same language. At least in Falabella, Analytics is this language of communication between teams and within teams. So it is the driving force behind the standardization and leveling of methodologies and processes throughout the entire organization. It’s a natural process that doesn’t require much adaptation, since in their day-to-day work teams across the company use Analytics to mark achievements, report issues, do site debugging, and so on. In short, adapting the data-driven/data-informed model already comes with the added benefit of standardizing processes and fostering communication between teams.
3. Question by Ramon Flores: In a Retail as a department store, what would be your recommendation to achieve omnichannel when each division of the company has been an island, credit system on the one hand, ecommerce on the other, telesales in its world and the stores the same?
First of all, all media and devices have to point to a single Report Suite, or a single virtual base. For example, App, Web, Fonocompras/Telesales etc. They have to send their data to a common repository, so that all the company’s insights can be consulted in a consolidated way. Then, everyone will have to be trained in the use of data query and visualization tools. If they are mostly non-digital media, such as physical stores, they may still have some way to report data – for example, tablets that salespeople use in stores or digital kiosks that customers use on their own. With a foot in the door, store teams will become more involved in analyzing their digital media data, and comparing it with the rest of the business. This will inevitably generate more dialogue and a more collaborative culture between teams.
4. Question by Mauricio Miranda From where and how to retrieve or obtain the data to analyze in a small retailer?
In this case being small is more of an advantage than a disadvantage. Smaller companies enjoy more intimate relationships with the User (in all channels, digital or physical). In fact, a large company is specifically trying to replicate and massify the closeness with the user that the small ones already possess. A small retailer can then tabulate and categorize its users more easily and accurately, obtaining critical data through surveys, transaction tracking, broader criteria bases, other relevant data and observations. By cross-referencing this data, very powerful insights can be obtained (which then need to be validated with AB-tests). The only obstacle/limitation here would be the possible lack of know-how to store, process and cross-check this data; assuming that a small company will naturally have less development/data science resources. But these tasks are no big deal either – by dedicating a little of your own resources and/or getting consulting, the value of the insights is drastically multiplied.
5. Question by Manuel Eduardo: Eugene, you mentioned that if you don’t start becoming more data-driven in the industry, you are in the past – how did you manage to encourage a more data-driven and less anecdotal culture in your company?
The trick is to convince Owners (Product Owners and Product Managers), Developers, UX that there is no better way to demonstrate their work than Analytics dashboards. It is not that difficult either, as they are people with a “scientific” mindset with quantitative perception. A classic example: a comparison of the conversion rate before and after the implementation or modification of the product in question. By the way, being quantitative and methodical people, they will want to have confidence in the data. For this, I teach all the teams how to debug the data collection. Luckily the process is public and transparent. So anyone can be assured of the veracity/accuracy of the data they are analyzing. It also helps to make sure that top management fully understands and appreciates the benefits of Analytics. Once you have management, Owners and Tech Leads on your side, the rest of the evangelization flows seamlessly and quickly.