Powered by innovations in AI, Machine Learning, Natural Language Processing (NLP), and Deep Learning, MECBot solves the key enterprise data challenges in an end-to-end manner and enables insight-driven decision-making without relying on the underlying databases or the structure of the data. It is the go-to data analytics platform for several leading Fortune 1000 clients across the globe in Banking, Insurance, Retail, Sports, Healthcare, and more.
MECBot puts the business first by adopting the Entity Domain Model approach. It comes bundled with a self-service, intuitive interface and takes care of the key data management and analytics requirements in a trusted and centralized manner, including scalable deployment.
Simply put, MECBot automates the entire data unification process envisaging all forms of data at scale, and delivers unprecedented business results. To accomplish this, MECBot first structures the unstructured data contextually using domain-specific business ontologies and marries it with structured transactional data in near real-time. In this blog, we take a look at the top 10 use cases of MECBot that make it the #1 data excellence platform for businesses across all industries, geographies, and sizes.
Use Case #1: Customer 360o
Lack of a reliable and actionable Customer 360° view is at the heart of early abandonment by customers in their journey of interacting with a business.
The customer data wheel needs to run continuously at the pace of the industry and generate actionable insights in real-time. This is only possible when there is a single source of truth for all customer data. This implies the need for a Customer 360° view that seamlessly combines diverse customer data hubs like CRM, product databases, customer service records, industry reports on customer behavior, and multi-channel interactions of customers online.
Research suggests 79% of customers won’t consider an offer unless it is customized to their previous interactions (Marketo). Further, personalization is not a one-time affair, it is a constant reality.
Yet, Customer 360° is still elusive for most businesses. According to a study, as much as 66% of customer-analytics respondents cite linking data from disparate sources as their top challenge. According to another study, “fewer than 10% of companies have a 360° customer view, and only 5% use this view to systemically grow their businesses. Less than 10% of companies understand how to use a 360° view for growth.”
The underlying framework of MECBot’s Customer 360° feature consists of 3 key pillars as outlined below:
- Voice of Customer: Data on Customer Feedback, Reviews, and Inquiries.
- Voice of the Business: Product, Sales, and Marketing Data.
- Customer Profile: Demographic, Transactional, and Behavioral Data.
MECBot’s Customer 360° architecture is shown in the following image:
MECBot’s Customer 360° Capabilities
Customer Identity Resolution
MECBot’s stitches together first-, second-, and third-party customer data to create a single customer view. Businesses can then generate purpose-driven customer views based on logical rules and query them in natural English language. Customer identity resolution helps businesses to uniquely identify or “spot” each customer in any online or offline environment. This enables micro-personalization at a granular level without any loss of transparency.
Through such deep personalization, MECBot generates possible next best steps for delivering personalized customer experiences, capitalizing on up-sell and cross-sell opportunities, augmenting customer engagement and loyalty, and maximizing the customer lifetime value.
Customer Health Scores
Customer health scores don’t just pinpoint crisis segments. They also identify the low-hanging fruits, i.e. opportunities that businesses can capitalize on right away.
MECBot helps to answer business-critical questions on customer health, like:
- Since when have customers been using the products and/services of a business? How many products and/or services are they using? How effectively are they using the same?
- Which products are popular in what segment and in which region?
- Which customers are about to leave? Why? What can businesses do to stop them?
- Which leads are ready for purchase? Which product/service are they most likely to buy? Which channel are they most likely to convert in?
- Which customers are looking for an upgrade or a new offering? How can it be customized to their current and future needs?
Use Case #2: Smart 360o Dashboard for the World’s Largest Network of EV Charging Stations
In 2019, a leading electric vehicle infrastructure company based in California joined hands with us to augment the performance of their Finance and Marketing teams. They wanted to achieve the same with a 360o view of their Charging Stations across different regions with granular, real-time insights on:
- Usage of Charging Sessions
- Energy Consumption
- Driver Demography, and
- Station Utilization
The company operates an open electric vehicle charging network and also manufactures the technology deployed in it. It is the world’s largest network of electric vehicle (EV) charging stations in the U.S., Europe, Australia, and is spearheading the green revolution in transportation across the globe.
Our intelligent EVSE-Analytics application is built on top of MECBot as a smart, one-stop, go-to destination for EVSE players to maximize their RoI through intelligent segmentation, accurate targeting, cross-selling, and personalized promotional campaigns. It deploys a 3-layered approach to drive the Next-Gen Electric Vehicle Charging by combining Innovation in AI, ML, and IoT.
First, through smart interaction between Electric Vehicle & Charging Station, data from EV cars are sent to EVSE through Wireless, Bluetooth, etc. Our Edge Application sends the data to the cloud using the OCPP 1.6/2.0 protocol or any other protocol, as specified by the client. Then, another layer of smart interaction takes place between Charging Stations & the Central Management System (CMS).
To enable this, data across all the EVSEs in the network is stored as a smart enterprise graph and insights are extracted in real-time, such as which EVSEs are heavily used and how much load is being taken from the grid, Time-Series analytics, ML/DL algorithms for instant Anomaly Detection, insights and alerts on Predictive Maintenance of EVSEs and third-party integration to push relevant, real-time alerts to all groups of stakeholders. These are displayed on a smart, interactive dashboard.
The Machine Data is streamed in real-time along with a consolidated view of Station Operators, Downtime Prediction, NBA-based on anomalies, integration of Payment Gateways – all of this at industry scale along with the option for dynamic scaling up and down for higher efficiency.
In the final leg, our innovative and user-friendly mobile application fosters end-user engagement in real-time, while refreshing the User Identity Graph based on user inputs and records generated by the mobile application. End-users can reserve EVSEs remotely, plan trips ahead, get real-time alerts on EVSE downtime, view alternative routes, estimate the potential time to charge, obtain an estimate of how much to charge, explore places to see while EV is getting charged, etc.
Together, the system learns continuously and compositely from user behavior and forms a dynamic, semantically enriched graph in real-time. Using the location finder, users can also see the exact locations of all the charge points.
Use Case #3: Customer Churn Rate Optimization
In the U.S., companies lose $1.6 trillion every year owing to customer churn.
Customer churn occurs in a business when customers stop using the business’ products and/or services for good, usually switching to a competing provider in the market. This means that despite grabbing new customers at blazing speed, enterprises keep leaking their existing customers at a spiraling rate, leading to a lion’s share of the customer base getting wiped off. This leads to huge sunk costs and opportunity costs that can pull down a business in no time.
Our flagship unified data analytics product, MECBot, is equipped with the capabilities to detect, prevent, and reduce customer churn at scale for all kinds of businesses in real-time.
MECBot Enables Churn Analytics to Predict and Prevent Customer Churn Using AI.
- Estimates churn probability and ranks customers using churn scores.
- Automatically adjusts and updates churn values in real-time.
- Rates and ranks various factors causing customer churn like poor onboarding experience, unresolved issues, incompetence of customer-handling teams, better offer by competitors, poor product/service quality, and so on to identify the root cause of churn in businesses.
- Identifies proven, data-driven steps to drastically reduce the Customer Acquisition Cost (CAC) by lowering the churn rates proactively.
- Intelligently targets both voluntary churn (e.g. a customer choosing to opt-out because she/he is dissatisfied) and involuntary churn (e.g. a subscription customer forgetting to pay the bill or not remembering to renew the contract).
MECBot Enables Improved Customer Retention Through Loyalty Analytics, Retention Improvement Campaigns, and NPS Profiling.
- Ranks and scores each customer based on their loyalty and value-generation ability.
- Automatically identifies the next best step for each customer based on their loyalty rank and Net Promoter Score (NPS).
- Helps to target loyal, high-value customers with premium retention offers.
Use Case #4: MROI Optimization
Despite advances in the understanding of Marketing ROI (MROI) analytics, 43% of marketers say that “they lack the tools to transfer that data into real-time action”. Further, only 7% of marketers report that they are effective in ensuring real-time and data-driven marketing campaigns across both digital and non-digital customer touch-points, while over 59% of them are facing challenges in unifying their multiple and disparate data sources.
MECBot comes with inherent superpowers to keep you on top of the MROI game. MECBot is not a band-aid fix for Marketing data problems and insight gaps. Instead, it is an overarching data solution that helps you to build an ROI-focused marketing ecosystem that sets you up for success and market-winning decisions with accurate and actionable insights.
MECBot helps marketers to:
- Automatically run a trusted Marketing ETL (Extract, Transform, and Load) pipeline to clean, pre-process, blend, and assimilate all data that can be visualized using MECBot’s native reporting or external visualization tools.
- Make mission-critical optimizations to their marketing mix on the go by analyzing CPM (cost per mile), CPA (cost per action), and ROI in real-time.
- Easily switch between granular and singular views by revealing MROI across geographical, campaign, and media levels.
- Integrate Marketing outcomes with company-wide data like sales, profitability, cost-to-serve, customer acquisition cost, customer lifetime value, and more.
- Auto-detect patterns and generate insights on demand through datafolding and free flow search.
Use Case #5: Capture a Larger Share of Customer’s Wallet
MECBot helps you to proactively capture a higher share of the customers’ wallet by up-selling and cross-selling products and services to each customer segment that they will find the most useful and enticing. It correlates the movement of customers and segments across pricing tiers and design offers that make the most sense to each group. It also tracks, monitors, and provides recommendations to optimize metrics like ARPU (Average Revenue Per User), ARR (Annual Recurring Revenue), MRR (Monthly Recurring Revenue), and RFMC (Recency, Frequency, Monetary, and Clumpiness) to reduce Customer Churn Rate and maximize Customer Lifetime Value.
Grabbing a larger pie of the customer’s wallet is all about hitting the right touchpoints in the customers’ journey with the marketing vehicle. In this process, MECBot becomes the trusted recommendation engine to intelligently micro-segment customers and create offerings and communication campaigns that make them gravitate towards your business.
The challenge of increasing the share of the business in the customers’ wallet is often particularly important to take head-on in industries like e-commerce, where profitability is derived from repeat purchases and unflinching loyalty. MECBot is an integrated solution to make wallet capture an inherent strategy in every business decision made by decision-makers.
Use Case #6: Intelligent Customer Segmentation
Segment-based marketing is nothing new, but the real challenge is to spend marketing resources in such a way that it minimizes the Cost of Acquiring a Customer (CAC) while maximizing the return on such investment through smart and profitable segmentation.
This is where MECBot takes you several steps beyond traditional marketing analytics.
MECBot helps you to perform intelligent segmentation based on customer demographics, credit rating, transaction patterns, social media listening, sentiment analysis, behavioral patterns, and much more. But, that’s not all. When relying purely on traditional tools, customer segmentation is a challenging and tedious process that requires data scientists to keep querying the data manually for days or months in the hope of stumbling upon innovative segmentation approaches that go beyond traditional market segments.
But MECBot’s patented DataFoldingTM algorithm can auto-detect patterns and statistical regularities in data and cull out recurring patterns across various clusters. MECBot helps data teams to find profitable customer segments that would otherwise be near-impossible to spot through mere intuition and manual examination of the data at hand, saving countless man-hours and resources.
MECBot overcomes the limitations of traditional segmentation analytics by throwing up more targeted segments that otherwise would have remained unnoticed. Further, it doesn’t stop there. It takes it to the next level by automatically recommending campaign ideas and changes to be made in the existing campaigns such that deeply personalized campaigns can be run for each segment.
Use Case #7: Improving User Engagement in Sports Media
For a major Fortune 1000 sports analytics company, the major challenge was to retain their website visitors for a longer duration on their portal through the delivery of dynamic insights on live and past cricket matches, player analytics, and team performance analytics. The portal visitors included various groups or segments like cricket fans and general audience, team managers, match broadcasters, coaches, and sports editors.
The company was sitting on an enormous volume of cricket data spanning several decades but did not have the right technology tools to operationalize it in an engaging format that would engage its current visitors and attract more visitors through improved user experience and greater value proposition.
Traditional sports analytics platforms do not support query in the natural language, and hence, do not enable portal visitors to play with the data, derive enriched meaning based on past data, and predict future match outcomes and player performances based on factors like weather condition, toss result, team composition, location, and so on. Further, the insights need to be presented in a visually intuitive and attractive manner with appropriate filters so as to enhance the user experience of each visitor group.
The key features of the platform are:
|Query in human language||E.g. “Fastest 100s in ODI” Or “Player performance in a ground as a bowler”|
|360o player analysis||The current performance compared against seasonal averages, weak positions, strong positions, current opposition in current pitch, etc.|
|Look-alike analysis||The current player is analyzed against look-alike (similar players) and deriving data-driven strategies for the current match|
|Specific, intelligent comparison||“Warne in Bouncy Pitches” or “Lara against Lee in bouncy pitches”|
|Near real-time coverage||Ball by ball alerts sent during the match|
|Team performance analysis||Prediction of Team performance in the current match based on historical data, current environmental data like bouncy pitch, moisture condition, etc.|
|Classified access||Different views of data, e.g. Managers & fans see different dashboards each|
Here’s how this solution impacted the sports analytics company:
Use Case #8: Intelligent Banking Analytics
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, while MECBot uses RDFs (Resource Description Frameworks) to understand a specific domain, bankers can leverage MECBot with domain specific concepts like FIBO (Financial Industry Business Ontology) to understand and detect patterns across large volumes of data from multiple banks.
Use Case #9: Cash Management in ATMs for a Fortune 500 Company
With more than 20,000 clients in over 130 countries, Company X is a Fortune 500 company and one of the world’s leading financial software, services, consulting, and outsourcing solution providers in the world, powering billions of transactions every year. In 2017, the company was approached by banking authorities to address the pressing challenge of ATMs in India drying up on cash.
By joining hands with MECBot, this global frontrunner in financial services and technology consulting has been able to minimize cash shortage in ATMs, optimize currency denominations as per usage patterns of the population, and drastically reduce the number of roundtrips required between banks and ATMs to ensure round-the-clock cash availability.
MECBot for Cash Management in ATMs – Key Highlights:
- Daily cash forecasting at ATMs.
- Recommending the right mix of currency denominations.
- Sending real-time alerts for cash falling below the threshold at ATMs.
- Updating predictions with actual cash supply from banks on a real-time basis.
- Minimizing the no. of roundtrips required between banks & ATMs.
- Minimizing the service fees to transport companies and the penalty due to cash dry-up.
Use Case #10: Compliance Risk Management with MDM
According to the definition by Gartner, Master Data Management, or MDM, is a technology-enabled discipline in which business and IT work together to ensure uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared master data assets.
Simply put, Master Data covers those elements of a business that are the most valuable and important for its functioning. Customers, products, and locations are all master data domains. 89% of executives agree that inaccurate data impedes their ability to provide consistent customer satisfaction. Further, without proper governance of data, there is limited accountability and scattered ownership of data, which creates compliance risks as well as higher expenses and lost revenue. This is why MDM is being increasingly adopted in business practices. In fact, according to a survey undertaken by Forrester Research, 47% of 274 professionals said that the scope of their MDM programs includes more than two data domains to master.
Compliance risk management is an extremely important function for most businesses. The top management has to ensure that the business functions fall within standard regulations and norms. Non-compliance attracts fines, penalties, and tarnished reputation, and could lead to bad publicity and loss of business.
MDM in MECBot can be used to manage compliance risks as well. Companies can use historical data of non-compliance to build a database of commonalities, which can then be analyzed to come up with ways of ensuring that regulations are being complied with. It is also important to stick to regulations about customer practices, which can be done by analyzing customer data and identifying key areas where there are drawbacks. For example, MDM is helpful while dealing with mandates such as Know Your Customer (KYC), which requires a large amount of data to be collected. Data governance is an important part of regulation compliance, which can also be tackled by using MDM within a controlled data governance framework.
FORMCEPT believes that unless data is managed well, accurate insights from downstream analytics cannot be obtained. This is because in analytics, garbage in = garbage out. MECBot provides all these features as part of its Augmented Data Management module. It ensures clean, end-to-end data pipeline creation which is repeatable and hydrates in real-time. Then, once all the data is available, MECBot’s in-built plugins and pipelines come in, so that powerful AI and ML algorithms can be created without worrying about the underlying infrastructure or complex configuration of different technologies. This way, only the best-fit ML algorithms can be selected to obtain the best and the most accurate results for the data in question and the decision-making needs at hand.