By Ambar R. Kakkar, business designer at Accenture Interactive.
The lack of effective data sharing practices within organizations leads to uninformed decisions, missed product and service opportunities and an average customer experience.
Today, organizations collect vast amounts of data ranging from sales data to customer feedback to marketing spend and so on. However, in most organizations, data is siloed within specific departments and is not efficiently shared. Sometimes departments do not even know that another part of their organization collects data they so desperately need! In fact, UAE business leaders stated that over $2.1 Million is lost annually due to challenges in day-day data management – similar to the global average (Source: TahawulTech).
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Eventually, inadequate data-sharing practices lead to organizations failing to keep pace with rapidly changing customer expectations and, in effect, missed product and service opportunities. The gap between customer expectations and organizational offerings gradually widens and contributes to customer churn and revenue loss.
Apart from the missed product and service opportunities, the absence of an effective organizational data sharing practice leads to an average customer experience. For example, airlines possess enormous amounts of data on customers ranging from most commonly booked seat type to flier status to holiday accommodation. This data could be leveraged to provide a more personalized experience to customers. However, as some of this data is stored with the booking team, some with the loyalty team and other data with the holiday’s team and none of this data is shared, stored and analyzed systematically, personalized customer moments are limited. Indeed, Experian states that 69% of organizations agree that inaccurate data undermines their ability to provide an excellent customer experience.
So, how can organizations leverage data and become part of the business of experience that is becoming increasingly important?
Cloud computing, also known as cloud, can be a powerful solution. Cloud computing is the delivery of computing services – including servers, storage, databases, networking, software, analytics and intelligence – over the Internet, “the cloud.” (Source: Microsoft Azure). It can enable organizations to share and analyze data across the organization efficiently. In turn, employees can access a vast amount of customer-specific data and use it to develop new products and services and create personalized and meaningful interactions.
Below, I have listed the process of how organizations can adopt using cloud to identify new product and service opportunities and provide a more personalized customer experience.
1 Migrating your data to cloud platforms.
Today cloud platforms such as Amazon Web Services, Google Cloud, Microsoft Azure, etc., enable organizations to migrate vast amounts of company data from on-premises data storage centres to their cloud servers. Apart from effective data sharing and analysis (the focus of this article), some additional benefits of this migration to the cloud include:
Cost savings: Companies do not need to spend resources on maintaining their data storage centres. Additionally, most cloud services operate on a pay as you go model – which means you only pay for the cloud services you use. In a survey done by Accenture Amazon Web Services (AWS) Business Group, almost half of all respondents listed lower infrastructure and storage costs as a major benefit of migrating to cloud.
Higher security: A cloud host’s full-time job is to carefully monitor security, which is more efficient than an in-house system, where efforts are split between IT development objectives and security. Rapid scale (a global cloud services provider) claims that 94% of businesses saw an improvement in security after switching to cloud.
Flexibility to develop new digital solutions: If IT teams are spending too much time on data storage and management, they cannot focus on creating digital products and services to meet customer demands. Cloud removes the burden of data storage and management and empowers the IT team to spend their time developing the best products for customers. In the same survey done by Accenture AWS Business Group, 34% of respondents stated that cloud migration has allowed them to develop new innovative products and services, 40% specified that cloud had enabled the integration of organizational data to re-engineer products and/or predict customer behaviour and 36% declared that cloud adoption had reduced time to market.
2 Analyzing the data
Here is where the true value of moving the data to cloud begins. Once all your data is on a cloud storage platform, you can use SaaS tools such as Snowflake, BigQuery (part of Google Cloud) or RedShift (part of Amazon AWS) to begin to clean, structure and analyze your data. For example, suppose we retake our hypothetical airline brand that has recently decided to adopt Redshift. The SaaS tool can enable the organization to pull in customer profile data, travel history, most commonly booked seat type, vacation accommodation, etc. The data can then be mapped against each other, analyzed and used to create meaningful insight. Without cloud, this process would have required an individual to approach these various teams, collect the data, structure it and then analyze it, something that could have taken weeks!
Of course, organizations need to hire data engineers, analysts and business intelligence teams to ensure data analysis and the creation of meaningful insight. Such teams can create dashboards that enable customer strategists and product owners to identify product and service opportunities and design more personalized and relevant experiences.
3 Using insights to identify new product and services opportunities and make the customer experience more personalized
Once organizations have migrated their data to the cloud and have business intelligence teams set up, they can identify new product and service opportunities and provide a more personalized experience.
I have provided two examples of organizations that have used cloud to achieve these objectives.
- US Foods – Personalized retention offers:
About the company:
US Foods is one of the US’ largest food distributors and provides approximately 300,000 restaurants and food service providers in the country with products, culinary equipment and supplies. Restaurants and food service providers can place their orders using US Foods’ e-commerce platform.
The challenge:
Before migrating to cloud, the organization’s on-premises data warehouse required constant maintenance and the warehouse could not affordably store more than two years of data. Moreover, business analysts took weeks to prepare a single report due to the system’s inability to load large data sets and the time required to gather data. One consequence of these challenges was that US Foods was unable to have personalized interactions with its customers.
The solution:
To solve these issues, US Foods migrated their data into Amazon S3 (part of AWS). After migrating the data, their business intelligence teams were able to quickly access a wide range of customer-specific information. Their business intelligence teams then used Snowflake to analyze the information and generate personalized insights. For example, before removing products from its e-commerce catalogue, the organization analyzes millions of historical records on sales, customer profiles, customer purchase history and churn data to predict possible detractors. After identifying these detractors, US Foods develops personalized retention offers to ensure that they do not lose these customers.
Source: Snowflake
- Sainsbury – New product matching service
About the company:
Sainsbury is the UK’s second-largest retailer with over 1,400 stores and a significant digital presence. The company offers customers products across clothing, general merchandise, financial services products and food.
The challenge:
The company struggled with massive amounts of data that were siloed across the various entities of the organization. Gathering the necessary information to run queries required a high degree of effort, time and resources. Additionally, running a data query required 6 hours due to the sheer volume of data and slow computational speed. In effect, the organization was not developing products and services that matched customer wants and expectations.
The solution:
To optimize its analytical capabilities, Sainsbury collected all their data, re-modelled it and moved it to the Snowflake data cloud. The organization now had all their data stored on one system and could create data-driven insights and, in effect, new services for their customers. For example, by gathering the vast amount of product data from various departments and storing it on one system, Sainsbury launched a product matching service that compares its products with its competitors’ products. Additionally, they could also reduce data analysis query times from 6 hours to 3 seconds, allowing their business intelligence teams to provide more customer insights in a shorter time frame to the various departments. Hence, enabling multiple departments to create more personalized experiences.
Source: Snowflake
Conclusion
Organizations continue to struggle with siloed data and ineffective data sharing practices. By adopting cloud, organizations can have all their data located in one place, making it easy for various teams to access the data they require in a matter of minutes rather than hours or weeks. Additionally, by creating specific business intelligence teams that can use cloud to run multiple data queries across large volumes of data, meaningful customer insight can be provided to various departments. Although migrating to cloud and setting up the optimal analytics capabilities may require time, the insights generated can lead to personalized customer experiences, new product and service opportunities and overall, a more effective customer strategy that positively impacts business revenue.
