Digital disruption has brought about significant changes in the way companies do their business. With rapidly advancing analytics capabilities owing to the incorporation of machine learning and artificial intelligence, businesses are able to streamline their operations, create new revenue models, and elevate customer experience management.
Here are five digital transformation case studies wherein organizations filled the gaps in their operational performance, created greater value for their customers, and improved revenues with the power of data and advanced analytics platforms.
A technology company saves $330 million in revenue
A Fortune 500 technology company has multiple manufacturing partners, distribution channels, and customers at diverse geographic locations.
In this complex supply chain scenario, the company intended to have analytically driven, closed-loop, and integrated demand planning process.
The company improved its existing forecasting process by integrating new external data sources and developing machine learning ‘best fit’ algorithms.
- Product segmentation based on the forecastability
- Enhancement of the existing time series forecasting algorithms
- Selection of the best stream among the sales, distributor, finance, and marketing projections
- Exploration and augmentation of the sales pipeline, install base, and econometric drivers
- Forward-looking sales pipeline signal integration in the demand forecasting
- Organization-wise operationalization of the optimized single forecast by integrating with Demantra
- $330 million in savings
- 17% increase in planning accuracy
- 8% increase in lead time attainment
Read the full supply chain analytics case study to understand the detailed methodology.
A technology major improves sales forecast accuracy by 15%
A Fortune 500 technology company operates globally with many sales teams across various geographic locations.
The sales teams were facing challenges in meeting their quarterly sales targets consistently. Therefore, the company required a metrics-driven approach to make accurate bookings predictions and improve overall sales performance.
The company deployed an AI-powered sales enablement platform and forecasting algorithms to improve bookings predictions and identify ‘at-risk’ teams or teams that were less likely to meet their sales plans. It also established a ‘sales acceleration’ center of excellence, which led to better sales performance and effectiveness.
- Integrate data across different systems (SFDC, bookings data in SAP HANA, etc.)
- Define metrics for sales performance and design visualizations for automated reporting
- Analyze the drivers of deal conversions and frame thumb rules for the teams to assess potential
- Predict bookings using the pipeline and integrate the findings in the decision engine
- Identify at-risk sales teams and integrate other data sources like HR data and Salesforce data
- Improve bookings prediction algorithm and build a decision support mechanism
- 15% rise forecast accuracy
- 30% increase in the identification of at-risk teams
- Better sales performance owing to metrics-driven dashboards
Read the complete sales analytics case study for all the details.
A CPG major realizes revenue uplift of 19%
A CPG company was facing challenges in managing its SKU replenishment intelligently and improving sales growth. It wanted its sales team to have more informed sales conversations with store owners by leveraging a host of historical information.
To overcome the said challenges, the company used a recommendation system that uses machine learning/artificial intelligence to provide the sales representatives with intelligent, store-level recommendations at the point of ordering.
The illustration below details the methodology used by the company.
The sales representatives used the recommendations to have intelligent conversations with store owners and achieved higher conversion rates.
The company saw an increase in footfall owing to enhanced customer satisfaction. This led to:
- 19% revenue uplift
- 8% sales growth
For all the hows, read the full recommendation engine case study.
A leading hotelier achieves a 10% increase in repeat customers
A popular hotel was facing a common yet critical problem in the industry — dipping occupancy rates. The cost of acquisition was high and customer retention was quite a challenge.
The hoteliers faced challenges in integrating data from multiple sources. The client stitched these data sources to get a unified view of customers. The types of data used were:
- Customer survey and review (unstructured)
- NPS and other similar scores
- Demographic data
- Transactional data on value-added services
The company used an analytics platform capable of providing 360-degree insights on customer experience metrics by analyzing quantitative and unstructured information from multiple customer touch points.
- Analyze travel patterns at a site/resort level
- Forecast utilization rate at a site level
- Identify the most valuable customers
- Personalize communication with target customers
- 10% increase in repeat customers
- 6% increase in occupancy rates
For all the hows, read the complete customer analytics case study.
An insurance giant reduced cost per conversion by 15%
A leading insurance provider in the US, which specializes in property and casualty insurance, was selling multiple products across many channels and sub-channels.
Such a product-channel proliferation made it essential and equally challenging to optimize the marketing spend while maintaining a decent RoI.
The company wanted to build direct marketing models, optimize the product-channel mix at a customer level, and optimize marketing effectiveness.
- Build direct marketing models at a customer level for every unique product-channel combination
- Optimize marketing efforts to reduce spend and increase ROI by identifying:
- Right customer to target
- Right channel for every persona
- Right messaging for various promotions
- Right time to target
- Develop an alternative solution to replace the existing product-channel level models with an overarching recommendation engine
- Cost of database marketing reduced by 7%
- Insurance premium value increased by 10%
- Cost per conversion of a potential lead decreased by 15%
For all the technical details about the methodology, read the full marketing analytics case study.
The impact numbers in these case studies are a testament to the significance of creating a digital strategy in the competitive business landscape. However, this transformation is not a one-and-done process. Rather, it is a culture that will keep evolving as long as there are advances in the technology and analytics space.
So, how can your company keep up or get started?
Thankfully, there exist analytics and technology-as-a-service providers that can guide your organization through the digital transformation journey and ensure greater ROI. And BRIDGEi2i Analytics Solutions happens to be one of the providers that get it.
Drop a note to email@example.com, and collaborate with us to create a digital strategy that would work for you while making the most of our technology accelerators, data science expertise, and domain knowledge.
Think you’re aware of all the interesting application areas of artificial intelligence? Find out by checking out the video below!