Driving Success Towards AI - We dont Speak, Our Case studies does

Fusion.AI is proud to present solutions and case studies of our clients who are pushing their frontier towards AI successfully. Explore our case studies and solutions integrated with AI that are already enhancing the next level human creativity to resolve advanced possibilities in various industries.



Churn prediction and helps in smart customer engagement


The growing adoption rate of alternate fuel vehicles had resulted in a continuous decline in profitability. The client decided to respond to the challenge by empowering its fleet management division with advanced digital and consumer analytics tools to attract new customers as well as to retain existing customers.


Fusion helped the client come up with business requirements and analysis to narrow down their issues, to the following solutions:

  • Firmographic analysis which was used to understand growth opportunities for corporate fleet and offers.
  • Integrated market-research-based approach was used for identifying potential customer targets.
  • Lead prioritization methodology based on the sales funnel and marketing activities were prioritized

We used predictive analytics to build:

  • Cross-sell and upsell model based on market basket analysis
  • Campaign response model using logistic regression
  • Attrition model to prevent client churn
  • Improved customer acquisition efficiency
  • Realtime algorithmic scoring
  • Increase in ROI



Demand forecasts and logistics optimisation


The client wanted reliable demand forecasts for each of its service centers to obtain more efficient staff capacity planning and meet demand more effectively, thus boosting revenue and meeting customer expectations.


We helped the client’s team to understand and consolidate the historical data trends from its existing sales database for the past 5 years while taking into account factors such as weekday, monthly seasonality, holidays, and locality and beyond etc.

Post the exploratory data analysis, we helped the client to build predictive linear models to accurately predict the future demands based on observed historical trends. Then, using advanced classification models, our data scientists divided the client's service centers into categories and created buckets of homogenous service centers. The modeling exercise was repeated for each service center category and the factors were tweaked separately as per their specific requirements.

  • Delivered accurate demand forecast
  • Model helped client to create effective staffing resources
  • Decrease in lead time and improved efficiency

Data Visualization

Data Visualization

BI and data visulalization services for multinational company


The client was concerned that its different product category teams were overloaded with too many mandatory monthly reports. There were concerns about duplication of effort and excessive time invest for data consolidation, data preparation, and data compilation. Clients priority was to streamline all the reporting and increase efficiency by standardizing and automating all the reports


Our team of data scientist gathered all the required from all the team and reports. Team used Tableau and designed a standard report template. The template incorporated majority of the common factors in order to make reports look and feel similar and easy to automate.

Our data scientists leveraged their experience with user-friendly interfaces and data visualization to create final reports which were easy to understand, comprehend, and reliable, thus enabling the client's business teams to spend more time analyzing the trends and get a holistic view of different categories and draw meaningful comparisons.

  • Streamlined analytics report created faster turnaround time on business strategy each month
  • Standardizing the operating procedure made communication and collaboration with internal and external stakeholders easier

Sales & Predictive

Sales & Predictive

Sales and Predictive analytics for Retail Chain


The client was planning the launch of a new collection of its products across its stores in the US, catering to its main customer base - upper middle-class Generation X consumers. The client asked Fusion to create an independent sales forecast model to predict the turnover at the store and regional levels.


Our data scientists and retail experts decided to build a mixed model that combined autoregressive and economic features to account for economic factors as well as for proxies for brand perception and consumer trends.

Our initial model included 3 broad categories of factors:

  • Microeconomic and product-related factors, including product category, launch season, price, and marketing spend.
  • Macroeconomic factors including average personal wealth, average personal income, GDP growth, CPI (Consumer price index), CCI (Consumer confidence index), etc.
  • Previous sales across relevant product categories.

We conducted interviews with select experts from research and industry to cross verify the set of factors and get reference points for their parameters. The client provided us access to their marketing, sales and customer records. We gathered the remaining macro and microeconomic data from free and paid data sources. Based on the data we created a predictive model that would forecast the sales of new collections under consideration of various microeconomic, macroeconomic, and autoregressive factors.

  • Delivered a predictive model to forecast the turnover of the clients’ upcoming collection of new products
  • Due to the model’s flexibility, it could be deployed for other future products launches as well

Robo Advisor

Robo Advisor

Identifying high potential client prospects in wealth management

Robo Advisors (RAs) are usually perhaps the most significant disruptive development in wealth and asset administration today. A Robo-Advisor acts as a digital financial consultant driven by Artificial Intelligence (AI) and automation. It requires a customer by way of a series of problems about their budget, investment encounter, risk factors, and advises a personalized investment decision portfolio containing bonds, shares along with other asset types.

Business Challenge & Opportunity

The way buyers connect to their financial expert services providers is adjusting. To stay prior to the curve, our customer who is running financial firm giving top-notch brokerage services, and wealth management to retail customers wanted to kick off a fresh Robo-advisory platform for one of its retail customers to serve additional consumers online and enhance human advisor efficiency. With Robo advisors to be a huge disruptor within the financial planning and management marketplace, our client wished to launch their very own robo consultant. They wished to start with evaluating the product good results within their pre-existing client base. The task that they experienced was to recognize prospects of their existing clients without losing industry spend on the incorrect target audience.


Fusion Informatics built and carried out a targeting choice for our client in order to identify the proper prospects.


Fusion Informatics performed a 2-level research to comprehend the product market and account of potential customers

  • Market Study: Using general market trends, we collected info on the user profile of customers that are customers of Robo advisory services
  • Customer Survey: We conducted study within asample of the prevailing customers to comprehend their inclination for robo advisory products and services


  • The survey benefits were used to recognize the most encouraging prospects
  • These prospects have been further analyzed to comprehend their demographic and engagement behaviors
  • Top attributes incorporated variables like age, wealth and task rate


  • The segmentation factors were utilized to segregate clients who it within the profile identified
  • These customers had been the qualified through channels like phone call and emails targeted at marketing the brand new robo advisory support to these prospects

The robo-advisor we shipped was successfully examined internally, showing wonderful promise for common adoption. This innovative capability helped our client utilize underserved markets and provide wealth management tips to customers at scale.

  • 60% decrease in marketing spend
  • Response rate seemed to be 40% while purchase rate was initially 17%
  • 2.5x ROI has been achieved in the initial phase of marketing and advertising
  • Optimize revenue and marketing things to do, and increase earnings

NPS Solution

NPS Solution

AI Chatbot powered NPS to improve business

As an e-commerce merchant, Net Promoter Score (NPS) could be the most powerful toolkit to grow your business and win loyal customers. NPS is a metric that measures the probability that a client will recommend his business to his personal and / or professional social circle. The higher your NPS, the greater the chances that your customers will become active Promoters and brand advocates for your business.

Business Challenge & Opportunity

The Increase in completion in current market and very sensitive market conditions have been led to reduction in revenue and progress of our KSA based ecommerce client. We made an ecommerce system where the buyers will be serviced via an interactive chatbot. Our client wanted to far better understand customer encounter when using this platform and wished to make continuous enhancement to meet up customer's demands and increase success in this ecommerce business.


We Fusion Informatics helped our client tackle this condition utilizing the technology called NPS (Net Promoter Score) technique. We constructed a survey that could help client regulate how useful the system is to buyers and what is the opportunity for improvement as well.

Fusion Informatics implemented 3-step approach to work with NPS metric:

Analyzing Customer Experience Milestones

Customer action to an ecommerce platform could be gauged at two points

  • When customer parts the order:
    At this phase, the implemented NPS would evaluate how easy it really is to connect to the platform and simple investing or buying a product.
    In addition, whether sufficient information regarding the merchandise and owner is available to create the best decision
  • When goods will be delivered:
    At this phase, NPS would gauge the customer satisfaction following the receipt of ultimate product.

Information Collection

Once the milestones are finalized, data collection stage begins.

  • Following the order placed, combined with the confirmation email; buyer is sent the initial NPS review to give rating for the level of service. Also, comments concerning the area of development are collected for even more descriptive analysis
  • The next NPS survey is certainly sent after 1 month of item receipt. The next survey discusses the merchandise and delivery provider. Customers have a choice to mention responses like giving comments about the item, shipping and delivery service

Analyze the effect

  • Regression analysis was basically used to recognize major individuals of fulfillment and dissatisfaction
  • Word or text mining and sentiment analysis making use of NLP to extract words from reviews and comments categorizing negative and positive assessments and segmenting buyers thereof
  • Time series analysis was conducted to recognize in customers churn

Our analysis helped our client getting a deeper insight into the "actual" critical aspects influencing customer experience & loyalty. Considering the below results, it appeared that "customer efforts" was an important aspect that needed to be tied to the survey responses and was our recommendation for inclusion in the survey form. This enabled the client to focus on business opportunities while improving on agent’s opportunities at an organization level.

  • Improved customer support
  • Reduced consumer complaints
  • Increase buyer retention
  • Upsurge in profitability by 3x
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