With the proliferation of digital touchpoints, customer interactions, and online/offline channels, customer data is growing at an exponential rate and so is the number of customer data solutions and the data environments that they are associated with. In 2020, for example, there were over 8,000 tools and technologies available to capture, map, and analyze customer interactions (as shown in the image below). Collectively known as Martech or Marketing Technologies, this domain has seen unprecedented growth in recent years. Further, by the end of 2020, the customer data segment was processing over 1 trillion API calls a month.
CMOs too are increasingly placing Martech at the forefront of their strategic decision-making and budget allocation, as shown in the image below.
While it is true that the increased availability of first-party customer data and the proliferation of Martech has made it possible for marketers to map their customers better, they are yet to translate into real value in dollars for businesses. As a matter of fact, the rapid boom in the customer data ecosystem and Marketing technologies has led to 2 key challenges that no single tool or technology can resolve completely on its own.
These challenges are:
- Fragmentation of the customer identity leading to audience visibility gaps and customer experience gaps: This means that since multiple identities of the customer are scattered across various digital touchpoints, on one hand, the marketer receives a distorted view of the customer (visibility gap), while on the other hand, the customer has a disjointed experience that frustrates her/him (experience gap).
- Breakdown of the traditional customer segmentation in Marketing leading to inaccuracies and time-lags in decision-making: Traditional customer segmentation is static, pre-defined, and relies on a few segmentation variables that are easy to identify, measure, and compare (like age, gender, location, etc). This segmentation has now broken down and is no longer effective. The need of the hour is to replace it with a dynamic, intelligent, and real-time micro-segmentation based on variables like psychographic factors, movement across the various stages in the customer’s journey, usage behavior, loyalty patterns, binge habits, and more.
First-Party User-level Data Can Make or Break Your Marketing
To solve the above challenges, Marketers require a unified platform to collect, organize, mine, and augment first-party customer data at the granular level, i.e. at the individual customer level. Such user-level data first need to be identified accurately using personal identifiers, which can then be used to personalize Marketing campaigns and track granular-level Marketing results even at the level of a single, individual customer.
Apart from merging the multiple CRM instances of each customer, Marketers must also stitch together databases like e-commerce engines, email management systems, content management systems, apps, Single Sign On (SSO) environments like Google and Facebook, anonymized data from website and app cookies, and more. This is known as Customer Identity Resolution and is a major leap forward in marrying rich first-party data at scale and in real-time with second- and third-party data.
Finally, Marketers have to blend the following together to create a single source of truth about the customer:
- information that is assigned to customers (like email addresses and mobile numbers) and is not changing on the go,
- information that is assigned to customers but is changing on the go (like location, transactions, change in preferences, etc.), and
- information that cannot be readily assigned to customers (like anonymous cookies and mobile device IDs).
In the post-pandemic world, Marketing is firmly rooted in delivering personalized and high-engagement digital experiences for customers. Experian’s latest Global Insights Report found that “60% of people have higher expectations of their digital experience than before Covid-19, increasing the need for businesses to make sure that they are leveraging data to benefit their customers, providing secure and convenient digital experiences.”
MECBot Has Inherent Capabilities to Provide a Single, Dynamic, and Granular Customer View
Our award-winning augmented analytics product – MECBot – is a leading data excellence platform for all enterprise AI needs, that is powered by innovations in AI, Machine Learning, Natural Language Processing (NLP), and Deep Learning. Building reliable, scalable, and powerful customer data applications on top of MECBot, that uses high-quality, machine-augmented, first-party customer data, is just a matter of a few clicks.
MECBot puts your business first by design. It adopts the Business Domain Entity-Model approach without any dependency on the underlying databases or the structure of the data, and directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model. This helps to create a single, consistent customer ID that serves as the unifying factor across Marketing channels, devices, databases, data lakes, Martech clouds, and offline systems. Customer identity resolution is, therefore, a natural outcome of using MECBot as your trusted analytics engine.
Craft Unique and Compliant Customer Experiences Using Trusted Data to Unlock Hidden Patterns
MECBot’s banking-grade security and special emphasis on trusted data, role-based access, consent flags, and opt-in/opt-out management enable you to auto-comply with overarching legal provisions of the likes of GDPR and CCPA. A single 360 degree view of customers across Marketing, Sales, Customer Service, and Digital Touchpoints helps marketers to run and optimize live campaigns in a data-driven manner on-the-fly and at scale. With MECBot, you can personalize every interaction, every time, and everywhere. MECBot also enables you to tailor your content and promotions to fit rich micro-segments based on various behavioral and transactional indicators, and generate viable tactics at the pace of customer interaction.
Example Use Case: Banking
Despite banks sitting on top of vast pools of rich data, the conversion of Banking data into real business outcomes such as improved RoI, increased customer satisfaction, fraud prevention & AML(Anti-Money Laundering) implementation remains an uphill battle. Only 30% of banks have effectively matched their analytics efforts with their business goals, and 94% of Banking firms can’t deliver on their ‘Personalization Promise’ to customers.
MECBot Analyzes, Annotates & Unifies Banking Data into Smart Data Fabric
This includes Product & Customer Databases, Agent Chat Data, Device & App Data, Clickstream Data, Reviews, Spending Patterns, Channel Usage, & more.
The Smart Data Fabric is Contextualized with Universal, Domain & Tribal Knowledge Bases
MECBot cleans, transforms, and joins together structured data, unstructured data, and poly-structured data from Financial Databases, Customer Demographics Databases, Banking and Financial Literature, Research Reports, Banking Data Warehouses, etc. in real-time and at scale.
Smart Data Discovery & Auto-Recognition of Patterns with AI, ML & NLP
Actionable Banking Insights such as Look-alike Analysis of Profitable Customers, Powerful Pre-built Models Like Customer Segmentation, Loyalty Scoring, Opportunity Mapping for Cross-sell and Up-sell, Predictive Analytics, Market Clustering, & so on can be accomplished on MECBot with just a few clicks. Further, MECBot understands FIBO & can detect patterns across large volumes of data from multiple banks.
How MECBot Works
Built by ex-Oracle & ex-IBM enthusiasts, MECBot serves multiple Fortune 1000 companies across BFSI, Sports, Healthcare, EVSE, Retail, and more. It deploys our patented datafolding algorithm to create a semantically enriched enterprise knowledge graph by unifying structured & unstructured data and enhancing it with powerful knowledge bases. MECBot enables enterprises to perform smart data discovery, auto-recognition of patterns, free-form search & configure ML, DL & Graph-based algorithms that can scale dynamically & utilize hardware efficiently.