DTC Consumer Goods Brands: Hidden CX Costs From Scattered Product and Policy Data

Your brand is losing real money. Probably due to poor product information and scattered policy information. And each time you go up in cost for support, and in cost for abandoned cart, and in cost for return, that’s adding up to losing money for your brand.

Why scattered data is a CX cost problem for DTC consumer goods brands

There’s no disorganization in how the data for most Direct-to-Consumer (DTC) brands sits amongst the various tools that were picked to get the information to complete a task. A simple spreadsheet that’s been created of product specifications; a Notion built out for return policies; a Google Drive folder which contains all of the various rules around shipping; A Slack thread from two months ago containing all of the various promotional exceptions – all of this data was added by a human for a purpose, and it was working at the time.

The problem is retrieval.

Customer emails in asking if bundle is eligible for current promotion running. Agent will have to dig for answer in 4 unconnected systems. In the meantime, spreadsheet will probably have different answer than Notion doc, and rules for promotions currently reside in Slack threads that the agent may not even know exist. So, agent will either have to guess, send up the chain to have someone else answer, or respond to customer asking for more time.

This is a sequence of actions that costs something. Usually LemonLime doesn't measure the cost of this sequence of actions.

Where the financial drain actually shows up for DTC consumer goods brands

The costs can be broadly categorized into three types. They can also be additive or even compound in nature.

Lost revenue at the point of purchase. 49% of shoppers have abandoned a purchase during checkout due to a lack of sufficient information or unclear product details. For a Direct to Consumer company doing $2m a year in revenue, even a tiny percentage of that abandonment caused by having inconsistent product pages (in addition to being against company policy) that were previously OK’d in pre-purchase chat will quickly add up. When information about a product is held in multiple systems, different touchpoints will provide different information to customers. The brand may not even realize that the information is inconsistent, but the customer comparing the product page to pre-purchase chat to the email that they received weeks prior will very quickly realize the discrepancy.

Longer handle times and higher support costs. 3 in 10 support agents cannot reliably access customer information, which leads directly to irritated customers and longer resolution cycles. In a DTC context, "customer information" means more than order history. It means current policy, current product specs, current promotional rules. An agent handling 40 tickets per day (10 hours) who in each case takes 3 minutes longer to resolve their query (2 minutes per ticket) equals 200 hours of additional work for 5 agents over a month. The work does not generate any additional output.

Return Due to Misaligned Expectations. Return occur when customer read product page and have expectation that is not meet by what customer receive. When returns policies are fragmented between chat and returns team then brand’s intention is not communicated to customer and brand takes a very negative review plus cost of return shipment. Returns are very expensive. A single return of a $40 item can wipe out profit on that sale after processing return, return shipping and restock charges.

None of these line items shows up on a P&L labeled "cost of scattered data." They show up as support costs, refund rates, and conversion gaps. The fragmentation is the core problem. It is invisible until someone follows a thread.

How knowledge fragmentation drives CX costs across the DTC customer journey

Your customers’ experiences don’t stop at the point of purchase and location. You’ll follow them through their whole discovery process and continue to follow them through all their repeat purchases.

Pre-purchase: A user visits a product page and shortly after asks a question regarding an ingredient or compatibility in a chat with a bot. If the answer of the bot does not match the information on the product page (e.g. one description has been updated and the other not yet), trust is lost before the user places his very first order. No mail is sent and the user leaves the website.

At support: The customer who has actually purchased from you is typically delivered to the support agents who are working from the same disjointed information set as the customer. The support agent responds based on their memory or the first document that they find. Often that document is outdated. The agent delivers a wrong answer with great confidence. Follow-up tickets are very expensive compared to first-contact resolution. Always.

Post-purchase: By having return and exchange policies defined for products in multiple tools, there are two new failure modes for the team. First, there is a high likelihood that team will implement return and exchange policies inconsistently, sometimes approving and other times rejecting returns and exchanges. This will create much anger with customers who perceive unfairness. The second failure mode is that team will always approve return and exchange in order to avoid conflict with customer. While this will make customer happy, it will quickly destroy team’s margin.

Organized knowledge cannot be ignored, unorganized knowledge cannot be trusted, untrusted knowledge is bypassed by mere guesswork and that costs money.

What fixing scattered product and policy data looks like for DTC brands

No new policy wiki to remember to update – so many teams start out attempting to document with a new wiki for each new set of policies, never getting past the initial content of putting up the policies in the first place.

Creating a knowledge layer would resolve this issue. A knowledge layer would ingest all current knowledge and then organize it so it can be retrieved when needed. It will automatically update as the business evolves.

When an agent asks "does the current promotion apply to bundles?", the answer should come from a system that pulled the latest promotional rules from wherever they live, HubSpot, Slack, Google Drive, and surfaced the authoritative version. Not the version from last month. Not the version someone remembers from a team meeting. The current one.

This is what LemonLime does for DTC consumer goods brands specifically. It connects to the tools the brand already uses, builds a knowledge layer from the data inside them, and powers AI that retrieves and reasons over that layer. A change in product specs in one of the tools automatically updates the knowledge layer. A change of a return policy in Slack which then gets confirmed in HubSpot gets added to the structured source of truth for that information as well. Agents get the one right answer for questions around returns. Customers get the same answers as well.

A DTC brand running lean wants fewer escalations, shorter handle time, lower return rates due to mismatched expectations and fewer cart abandonments due to conflicting information.

How DTC consumer goods brands can get started without an IT project

For LemonLime no data migration, script or IT ticket is required.

The setup follows three steps.

1. Connect up the tools you currently use. Link up your existing tools, where your product information, your company information and your customers’ purchasing history reside already. Such as within Salesforce or HubSpot, or within Slack or Google tools or the Microsoft tools that you currently use. They connect up without the need of a pipeline.

2. The knowledge layer self builds – LemonLime automatically imports and structures data as it is needed for the AI to search and reason over. The knowledge layer automatically improves as LemonLime is used and updates automatically as your business evolves without the need for human maintenance.

3. Your CX operation runs from one source of truth. This is the structured layer from which agents and AI workflows are pulled. The answer to "does this bundle qualify?" is the same whether it comes from a chat widget, a human agent, or an automated flow.

First for DTC companies, pick the biggest pain point in your company where you have a lot of data points going at it. Then feed that into the AI once you connect all the tools. One test case will open your eyes to the ROI of the rest of the system.

LemonLime is currently on waitlist. DTC consumer goods brands ready to put a number on what scattered knowledge is costing them can join at lemonlime.ai.