2023 is the year of Customer Experience (CX).
86% of customers say they would pay a premium to brands that offer a fabulous experience. Companies with an annual revenue of 1 Bn USD can earn an added 700 Mn USD within three years of investing in CX. There’s no doubt then that enhanced CX is great for the bottom line.
This shift in customer sentiment has not gone unnoticed. 81% of organizations currently cite CX as a competitive differentiator. This year, however, enterprises remain cautious. The International Monetary Fund says that the risk of recession is lower than previously predicted. But the risk does remain. And so, CX strategies are focused primarily on retaining existing customers.
The key to this strategy is data. And enterprises have data in abundance. This is especially true in the banking, fintech and travel sectors. Demographics, preferences, and transaction information are integral to their functioning.
Making sense of this data is a completely different game. Enterprises in these verticals face four distinct challenges.
1/ Data is not accessible to all stakeholders.
71% of banks underperform at collecting and using customer data. Only 8% of banks can apply predictive insights to make decisions and run campaigns. Even digitally savvy fintech companies struggle in this space. Globally, 81% of fintechs say data issues are their biggest challenge.
2/ Available data is of poor quality.
Inaccurate data costs enterprises 15% to 25% of revenue. “Dirty data” costs the global banking industry over 400 Bn USD annually.
3/ External data is not used.
92% of analytics professionals say their firms need to increase their use of external data sources. Organizations that use external data saw 37% more revenue per employee.
4/ No single view of customer.
Only 28% of banks can integrate structured customer data to use in AI-led initiatives. There is a skewed view of the customer’s lifestyle and financial preferences.
Enter Data-as-a-Service (DaaS).
DaaS helps enterprises collect, manage, and analyze data. It makes information available across departments through the cloud. And provides secure and affordable access to data-centric insights. To make datasets understandable and actionable, there’s a lot of strategy, science, and structuring involved.
It’s not simply setting up algorithms. To uncover insights from datasets, a human touch is required. As is a scientific approach. Businesses can use these insights to plan their CX strategy.
The most effective DaaS capabilities take a multi-pronged approach. Let’s explore three key elements.
Data Lakehouse
In the words of author DK Moran, “You can have data without information, but you cannot have information without data.” An Accenture study found that 80% of organizations are sitting on unstructured and inaccessible data. Without this data to analyze, there are no insights to be had.
A DaaS provider ensures that the vital first step of data collation is done right. By opting for DaaS, enterprises can spend less time and effort in administration. With a cloud-based approach, the provider creates data lakehouses. All the data in one place, no silos.
This data management architecture leverages flexibility, cost-efficiency, and scalability.
- It reduces data movement and redundancy.
- Stakeholders have direct access to data for analysis tools.
- It is a cost-effective data storage solution.
Data cleaning
DaaS goes beyond just filling up the data lakehouse. Once your data is accessible, it’s time for some spring cleaning. The data needs to make sense before it is analyzed.
Enterprises often have inefficient methods of data collection which results in unclean data. This can result in
- Transactional risks – inaccurate or fraudulent transactions
- Incorrect assumptions about data-driven insights
- Reduced speed and productivity
Fixing this manually is a time-consuming and labor-intensive process.
DaaS saves the day with AI models and algos that improve data quality by removing inaccurate, corrupt and duplicate data. This ensures enterprises have access to high quality data. When analyzed, this can generate reliable visualizations and models to plan business strategies.
Regular cleaning also helps maintain effectiveness. Enterprises can then meet the five benchmarks of quality data:
- Validity
- Accuracy
- Completeness
- Consistency
- Uniformity
An efficient DaaS partner can also suggest better methods of collection to prevent or lessen the need for data cleaning in the future.
Data enrichment
Once a data-based strategy is in place, it’s time to focus on the customer profile. With internal data, banks can use AI/ML models to give top categories and merchants for each persona.
DaaS providers can go beyond this first-party data. They also give access to external data to create a more comprehensive view of customers on an individual level.
The benefit? Understanding customer behavior, customer targeting, and improved customer experience, to name a few.
For instance, Crayon Data’s AI-led platform maya.ai has DaaS capabilities like the patented TasteGraphTM. This helps enterprises calculate the affinity between any customer and merchant, even if they have not transacted before. Combined with external data (of 7.4 Mn merchants globally) each persona gets a
- Taste Print
- Taste Match Score
- Spend Potential
maya.ai also features Customer Genome, an inference engine that combines, consolidates, correlates, and mines patterns from data. Like the human genome, it uses information from the customer’s DNA to drive business value.
The result: a comprehensive view of every single customer, across demographics, product holding, transactions, and channel usage. And the ability to seamlessly convert these insights into action.
To improve customer experience, enterprises need to work with multiple partners. Data analytics is a layer of intelligence that adds value in both B2C and B2B markets.
But is DaaS worth it? Or will it be a flash in the pan, an investment that enterprises will regret or simply not use enough? According to Gartner’s Hype Cycle, 2023 is when DaaS reaches the Plateau of Productivity. That is, we’ll see high growth adoption. Conceptually, it’s no different from Software-as-a-Service (SaaS).
A DaaS partner with the above capabilities can make personalization more comprehensive, convenient, and relevant than ever before. Using data to create exciting customer experiences (that keep people coming back for more) results in added revenue.
Who would say no to that?
Crayon Data’s AI-led platform, maya.ai, features four modules that work together to create customer experiences that drive revenue growth. The Data-as-a-Service (DaaS) component helps enterprises unlock the value of their data. Combined with our Recommendation-as-a-Service, Customer Experience-as-a-Service and Marketplace-as-a-Service modules, it puts enterprises on the fast track to accelerate their revenue flywheel. Want to know more? Speak to our AI experts today!
