Five Ways Small and Medium Businesses Can Better Understand Their Customers
As business interactions become increasingly digital, Small and Medium Business (SMB) staff can sometimes be overwhelmed by the volume of information around consumers’ product engagements, like website inquiries, product purchases, and customer support tickets. Customer intelligence functionality can make it easier for SMB teams to consolidate and use this data so they can better execute marketing campaigns, next best action strategies, category management, and merchandising. With so many SMBs still managing interactions on manual spreadsheets, now is the time to think big.
At AWS, we recognize resource-strapped SMBs want to understand customer lifecycles so they can grow their businesses. Our approach is called Customer 360 and it enables SMBs to understand to answer these key business needs:
- Who is our customer base?
- What engagements have they had with the business?
- How have they responded to promotions?
- What is their shopping history?
- Have they opened any customer support queries?
The benefits of Customer 360
Organizations that have taken deliberate steps to deploy and utilize a Customer 360 approach have realized quantifiable improvements across an extensive list of critical Key Performance Indicators (KPIs).
From a business performance point of view, customers have realized benefits such as a reduced cost in resolving a customer request, increased customer lifetime value, accelerated time to market, and improved NPS scores.
From an operational performance point of view, businesses saved significant time on activities such as customer data preparation, ad-hoc analysis, and insights generation, which led to faster customer query resolutions.
Maturity curve of Customer 360 approaches
Look at this as a continuum of value when evaluating how it fits into your business processes
1. Start with the Customer Profile
Raw customer profiles are a continuous collection of a customer’s behavioral, transactional, and interaction data. This includes events, attributes, device information, user consent, and much more. As data is ingested into your SMB’s cloud data lake, it is tied to unique customer profiles so that it can be used in generating metrics, insights, and responses.
2. Resolve Identities
Next, attribute the raw customer profile data across all channels, devices, and platforms to a single user profile. Factors to consider include anonymous vs. logged-in users, tracking across multiple applications and platforms, and customer privacy choices. Identity resolution is a key step in creating a consistent experience for customers regardless of how they interact with your SMB.
3. Enrich the Customer Profile
Data enrichment can make the customer profiles more accurate and insightful. Progressively ask customers a few questions when they interact or by using second- and third-party data related to the customer profile. For instance, categorize your e-commerce customers by Customer Life Time Value or by propensity to upgrade their subscription.
4. Connect and Contextualize
Understand your findings by connecting a customer profile with additional publicly available data such as traffic information (for a delivery service), local holidays, weather details, or major sports events to add context to likely order volume. No matter the size of your business, this type of data collection can help improve operations.
5. Sense and Respond
An enriched, connected and contextualized customer profile is paired with a “sense and respond” mechanism. Simply put, a customer’s feedback is sought continuously and is incorporated in building products, services, and messaging, to increase customer delight. This can lead to better customer retention.
New to digitization or looking to add more cloud capabilities to your SMB? Explore solutions by industry, benefit, use case, and more on AWS Smart Business
Challenges in building and implementing a Customer 360 platform
The business benefits of building a Customer 360 platform are clear, however, executing on the build comes with its unique challenges if you do it yourself. These customer information challenges include:
- Data integration: Merging structured and unstructured data from legacy and modern platforms
- Correlation and translation: Generating inferences and insights requires knowledge of Artificial Intelligence/Machine Learning technologies and the ability to use these at scale
- Performant storage: The need to continually gather new data requires a data platform that can support expansion and scale
- Timing: the need for real time (or near real time) integration capabilities throughout the data chain to allow for the customer profile to be used where and when it matters.
AWS and its tech partners help mitigate these challenges with:
- Data integration services which cater for various types of data sources such as relational databases, delimited files, and NoSQL databases
- Translation and correlation services and features that allow translation between a wide variety of formats
- No code AI/ML based services for generating inferences
- Proven storage services that perform at scale
- Services for real time data propagation to external systems
Customer 360 Architecture for SMBs
With a wide variety of AWS services available for meeting each of the challenges, the technology choices and workflows can sometimes be overwhelming for SMBs. In our experience, many teams spend more effort than necessary on building the customer profile using absolutely all the data available, rather than being selective about what would offer the most benefit.
With that in mind, we propose a simplified view of Customer 360 for SMBs, with the specific aim of keeping the architecture streamlined and easy to maintain and evolve.
What does this look like in practice? As depicted, the data lake built with AWS Lake Formation and Amazon S3 forms the heart of the architecture. Lake Formation integrates seamlessly with AWS Glue and Amazon AppFlow to provide granular data access policies through a simple grant and revoke permissions model.
For insights, inferences, and predictions, the data from the data lake is fed to Amazon Personalize and Amazon Athena. Amazon Personalize predicts user engagement based on domain-based or customizable recommendation models. Athena can provide analytical insights based on the data lake.
To start, we recommend SMBs focus on the inputs and outputs of the platform, ensuring they have ingested the appropriate data, and integrated with the required customer experience journeys. From there, you can iterate on and enrich the data to create useful insights. Each section can be evolved to add more services as business data grows and IT architecture matures.
To build a customer intelligence solution that delivers on the business benefits, SMBs should favor simplicity over exhaustiveness and accuracy over precision. The architecture diagram is deliberately structured in vertical swim lanes, with specific concerns. This allows businesses to attack the challenges in bite-sized chunks. Businesses should consider the below in order of priority:
- Customer journey data sources
- Integration and transformation
- Storage and access control
- Insights, prediction, and inferences
- Real-time communication