Digital Transformation through Connected Processes

Digital is an all-pervasive phenomenon and the industry is trying to wrap its arms around it to extract real business benefit it promises. It is omnipresent and all aspect of business, from customer at one end and supplier at other with production-logistics in between, can be transformed using Digital. Creating a compelling experience with every interaction with customer is one of the key benefits apart from other efficiency / financial benefits on back end. Companies are experimenting in different technology areas like Cloud, Mobile/Internet technologies, IOT, AI, ML, Deep Learning, Robotics, Big Data and experiencing some benefits. It is still in the nascent stage for most of the organizations and experiments are being done in for limited area/topic. Industry players are not certain of what needs to be done to achieve Digital Transformation holistically and reap benefits to its true potential.

There are three important components of Digital i.e. Data Capture, Data Analysis and Business Application of the same. It is critical to understand each one and their relative dependencies to define the road map for transformation. This paradigm is different than the one we are used to for several decades. There is no dearth of data points, there is too much data, too much variety and significant speed of evolution. The critical factor for digital transformation is to identify most effective and economic way of capture/usage of this data for transformation of business. 

This blog describes one method, if followed by the organization, can provide an effective way of using digital. The method involves CONNECTED PROCESSES. Let us take example of customer information.

Customer information in various forms is used in key processes like Marketing, Sales, Merchandising, Procurement, Warehousing, Delivery logistics, Customer service, Incentive planning for sales persons, Product design, Product planning / manufacturing, etc. With the level of customer centricity organizations aspire to build, customer information in one form or other will be used in all the business processes.

This customer information is available from various sources and in various forms. The information is available from internal systems like Customer master, Purchase Orders, POS information, Loyalty programs, Customer complaints, Returns, etc.  There are indirect data points like Response to promotions, Response to social events / campaigns run by the company, etc. There is a third capability of obtaining tertiary information from social media, wearables, online transactions, etc.      

It is a Many-to-Many maze which creates difficulty for an organization. The dilemma is how to decide on what type of data to collect, how to collect i.e. what hardware / software they need to invest in as well as how to use the data i.e. what treatment that needs to be given to the data or what information should be extracted from the data intelligently vis a vis transformational benefits expected to receive. On top of this, the technology is changing rapidly, posing further challenge of selection of technology which will not be obsolete at least till it recovers the investment made.  

One of the method to simplify this journey is creating a map of connected processes and mount a layer of digital on top of it.

Let us take an example of a process of point of sale transaction at a store where customer is paying for the goods purchased. The step by step process map of this sales closure process could be:

·         Scan the items

·         Scan the loyalty card / Telephone no.

·         Apply applicable discounts

·         Scan the customer credit/debit card or receive Cash and complete payment

·         Bag the item and give the receipt to customer

·         Say thank you

Each process step requires help from other processes e.g. when you scan item, the bar code reader scans it, goes to the Item master, searches information like make (brand), design, color, size, price, etc. and displays the same on the screen. Similarly, when the loyalty card is scanned, or telephone number is provided, the CRM system is accessed. It fetches customer information like name, address, status, etc. and displayed on the screen so that the employee at POS terminal can greet customer by name. Similarly, it performs remaining steps and it either fetches or updates one or many systems in the background.

Now assume that Digital transformation team working on enhancing the customer experience believes that the experience could be enhanced if we build some intelligence in the system and make it available to the employee at POS counter to create a lasting experience. One of the idea could be “Creating special PERSONALIZED discounts/ offers ON-THE-FLY in additions to the discounts published. Each person gets specialized discounts / offers which has a significant potential for increasing customer experience as well as improvement in business volume and basket of products that a customer would buy. It would really please a customer if he/she gets special discount when he/she returns to store after a long time and gets a coupon for the brand of cloths he/she likes for the next purchase. A person would be very happy to get special deals on furniture, home decoration when he has just closed a home. Customer would definitely come back if he/she gets a gift of shirt / dress (brand which store wants to promote) based on purchase volume. A deal on travel, based on a personal event like marriage, kid getting admission to college where he/she needs to travel. Customer getting a special discount on merchandise of his favorite football team. The base idea is to create these offers without asking customer but using an AI engine which uses the information captured from multiple sources. The diagram below depicts the new process  steps for the same process at POS and how it can be implemented.

This has all the classic components of digital i.e. multiple aspects of information gathering, storage and building an intelligence from the data collected to create a memorable experience for customer. The diagram shows a different mechanisms of data collection which need to be deployed. The variety is significant and so are the challenges around selecting an economic way to deploy these mechanisms. It is the relationship between “Mechanism of data collection”, “what components of data” and “How this component to be used” which guides the path forward.

Let us take example of two mechanisms depicted in the diagram i.e. “Social Media Sniffing” and “Returns”; one appears in every aspect and one appears for only one aspect.

Social Media Sniffing is a vast area which starts from Facebook, Instagram, Twitter, and goes on.  The objective of this sniffing is to create certain profile of customer with specific view point which is required to help achieve the objective of process underway. Let us consider “Customer Likes /Dislikes” as one such view point; we are using this view point to create a personalized offer to customer at the point of sale. However, the same view point can be used in multiple processes such as Online Sales, Mobile Sales, Campaign formulation, Merchandising, Product Design. These processes are connected, and the binding glue is “customer likes/dislike view point”. Each process may use a little different processing of sub components as per its need, but the base data is common. This exercise provides a clear list of what data components need to be captured for every customer. Once we know this, we can look at the tools/ technology available at our disposal e.g. is it sufficient to look at Facebook data only or we need to look at Facebook, Twitter, Instagram or may be Tinder, etc. Which then provides us the mechanism that we need to adopt, is it going to be only indirect method, or we need to look for some direct methods like surveys (which could be designed based on holistic requirements for more than one processes) or just use the data available in the CRM system.

We have seen that Social media sniffing can be used to create some other viewpoints also like “Customer lifestyle”, “Customer Health”, “Customer Personal Events”, etc. These viewpoints are now used in multiple different processes and we get some additional data points that needs to be captured using the same or different tools / technology. These are the important points that needs to be kept in mind while selecting technology for its holistic application.

Same principles apply to the “Returns” also. The returns information is used in multiple processes in the organization in different but unique ways. e.g.  Any “Feature Dislike feedback” from this info is useful for Product Design Process to see what could be improved so that product is attractive to the customer as well as it becomes part of Like/Dislike view point. Any “Non-Performance feedback” is useful for Manufacturing process to understand if any improvement needs to be done while manufacturing the product.

Any “Damage in transit feedback” helps the Logistics / delivery process to see if something needs to be improved. This feedback is typically captured in the returns transaction either through a form filled by customer (Physical / online) or the interaction customer service agent has with the customer at the point of return. Many a times, this may not be the real reason. Company can use other methods like customer purchase history, their likes and dislikes on social media etc. to collect and corroborate the data and get the real returns view point/ information for driving improvements. This aspect of collecting data from tertiary sources to corroborate the data collected directly takes it beyond aspects of “Returns” and gets connected with multiple other processes in the organization which are planning to use mechanism of “Social Media Sniffing”. It has created two maps of connected processes.  

Thus, creating connected process map/s help us understand the ultimate data that we need to collect and the technology that we should use for the same to bring in desired digital transformation in holistic manner and reap the desired benefits.