Win with Smart Commerce℠: 4 Capabilities Tech Solutions Need to Prevail in a Digitized World

 In Articles

In today’s digital economy, solid logistics, a fair price and a satisfactory product are table stakes – they are a foundational set of attributes among all viable players, yet these areas give businesses little room to edge out competitors and drive additional revenue. More is required of businesses to prevail as market leaders in the digital world.

As noted by Danielle Uskovic, the Head of Digital and Social at Lenovo’s, “Salespeople need to be more like marketers, in this new digital world, and marketers need to be more like salespeople.”

As consumers’ expectations for seamless, personalized and digital-first experiences continue to rise, sales and marketing teams must break free of their traditional roles to effectively collaborate with shoppers – Salespeople need to transfer customer information and general trend knowledge to the online world, and marketers must understand the end-to-end customer journey and tailor specific, individualized messaging to push shoppers through the sales funnel.

To separate from the pack and allow teams to meet new demands, it requires a smart, collaborative technology that masters transaction data management, is rooted in machine-learning, visualizes actionable data and has the capabilities for teams to digitally collaborate with customers.

Enabling this new form of Smart Commerce℠ will support shopper experiences that are more engaging, personalized and seamless. Ultimately, forging shopper loyalty deeper and driving significant revenue.

Transaction Data Management

  • Do you have a unified view of transaction data?

Why is it when customers visit a store, sales associates are largely unaware of items they have purchased online (if aware of ANY online or in-store purchase history)? Why is it when customers use online chatbots to ask if the same dress they bought in-store is available online, the attendant on the receiving end has no way of knowing?

Answer: siloed transaction data. With each medium available to purchase items, a separate system to manage the commerce data comes with it. The result? A lack of visibility into other systems of information, no unified view of product or customer data, and no way of managing or tracking the data across systems.

To enable truly productive collaboration, businesses must deploy a system that unifies all sources of transaction data (Point-of-Sale (POS), Customer Relationship Management (CRM), Ecommerce Platform). With a normalized view of product data and customer purchase history, businesses can manage commerce data across digital and physical brand environments.

Merchandising decisions would become data-driven, transparent and channel-specific. Floor sales associates could efficiently pull-up online and offline purchase history for every shopper that comes in-store. Customer service representatives could aid shoppers more fluidly and readily as customers bounce between digital and physical channels.

Implementing technology that consolidates transaction data should be the bare minimum in businesses efforts to create a holistic experience for their customers.

Machine Learning

  • Do you have the capability to derive insights and predictive intelligence from transaction data?

If businesses don’t implement a system that is rooted in AI, a tech solution will solely serve as a glorified excel sheet. Consumers expect more from sales associates than simply regurgitating a synopsis of their online and offline purchase history. Businesses need to leverage highly intelligent, unique and valuable data to create exceptional experiences and forge deeper customer relationships.

Coincidentally, businesses already have these valuable insights – they’re just hiding in the heaps of transaction data.

When commerce data is applied to a machine-learning solution, compelling customer and product insights are exposed in the masses of commerce data. In real-time, machine-learning technology establishes parallels between individual customers, an entire brand audience and their relationship to particular pairings or groups of products.

Machine-learning solutions can extend these learned relationships to make predictions about individual customers, or segments of customers, and how likely they are to interact with particular products. When accessible by a workforce, these predictive insights create opportunities to suggest personalized products or product pairings to individual or segments of customers. From a merchandising perspective, these insights can be leveraged to plan and determine product assortment.

No longer can customer experiences be enhanced JUST by data alone. The next generation of digital commerce requires more from merchants. Businesses must marry the intelligence of predictive suggestion with intuitive, human knowledge to enable collaborative dialogue that’s impactful and meaningful enough to be leveraged as a source of revenue.

Commerce CRM

  • Do you have the capability to surface a view of commerce data that supports collaboration?

The transaction data – and the valuable insights that are gained from machine learning – must be viewable and accessible in ways that actively support sales and marketing efforts to build customer relationships.

Ask yourself: In what context does product data need to be displayed to enhance sales efforts? What about marketers? Alternative views that pivot from these perspectives is critical to fuel actionable, insights-driven outcomes.

For example, data that can be viewed through individual customer profiles would equip salespeople with the necessary insight and predictive recommendations to target high-value shoppers and collaborate more productively one-on-one or on an account based basis, as they regularly do.

By the same token, organizing insights by cohort analytics would enable marketers to create campaigns and build promotions around customer segments. For instance, marketers could promote specific product recommendations that align with segment preferences and behavior.

A technology solution that can organize and view commerce data in this way, empowers sales and marketers to execute revenue-driving insights within the context that they interact with customers. Ultimately, this allows for ongoing, impactful engagement that consistently builds stronger customer loyalty.

Personalization & Collaboration

  • Do you have the capability to apply insights through data-driven personalization and intelligent collaboration tools?

To fully enable Smart Commerce℠, it requires technology that automatically personalizes experiences while allowing your workforce to digitally collaborate with customers one-on-one.

A system powered by AI can utilize real-time learned insights to dynamically alter product recommendations and site search to personalize customer’s immediate ecommerce experience. For customers that may not require the support of human assistance, these programmatic actions engage customers and improve their experience by providing intelligent, smart and valuable data-driven suggestions. Subsequently, these recommendations drive additional sales because customers are more likely to find supplementary products that align with their preferences.

The most important component of all, however, is the capability for sales associates and marketers to collaborate digitally with shoppers. Do you have the ability for sales to continually build and grow in-store customer interactions through online collaboration? Do you have a way for sales and marketers to apply their general trend knowledge and customer-specific insights through your website? Do you have a way to manage and measure these collaborative processes through notifications and performance reports?

Fluidly personalizing the in-store and online customer experience through AI and the nuance of 1:1 human interaction will support a consistent brand experience that is unique and valuable to your customers while reinforcing fluid relationship building opportunities for your associates.

Joining in the digital conversation is what is expected of sales associates and marketers. But more than meeting consumer expectations, it’s these effective, profound interactions that will increase businesses overall revenue, customer engagement, and customer loyalty.

By implementing technology that supports digital collaboration through transaction data management, machine learning, visualization of actionable data, and personalization and collaboration capabilities, businesses can empower salespeople and marketers to execute valuable, revenue-driving interactions with businesses most profitable customers.

Are you interested in leaning into the world of Smart Commerce℠? Let’s continue the conversation. 4-Tell is actively deploying this immersive technology into our customer base.

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