Navigating multichannel data to boost brand online performance

Have you ever encountered a word that exists in various languages but carries significantly different meanings in each? Not so long ago, an American astronomer faced such a situation while attempting to translate the map of Mars crafted by the Italian astronomer Giovanni Schiaparelli. While continents and seas were accurately translated, the term “canali,” used to denote channel-like features, was mistakenly translated as “canal.” The issue stemmed from the fact that “canali” in Italian refers to any type of channel, whereas in English, “canal” specifically denotes a man-made waterway.

As his work became known, this loss in translation led people to believe in life on Mars, inspiring the creation of “The War of the Worlds” within four years and giving birth to the alien science fiction genre.

While a miscommunication ended up favorably here, it is rarely so in the case of an organization. Unlike this fortunate miscommunication, organizations often face challenges due to poor communication. Multiple large organizations have not been able to understand their customer behavior leveraging the data effectively, leading to frustration from the customer perspective and eventual erosion of trust in the brand.

Similar challenges arise for brands operating on various Q-commerce and E-commerce platforms, alongside their native websites on platforms like Shopify. Handling data from different sources, including platforms, internal finance, warehousing, marketing tools, and market intelligence, results in fragmented data silos. These silos, in different formats across systems and functions, often lead to inaccuracies, duplications, and inconsistencies, causing confusion and misguided decision-making.

To maintain consistent and accurate critical data, brands need a holistic Master Data Management (MDM) approach. This approach manages product information, customer data, inventory details, and more across the multichannel ecosystem, addressing challenges posed by disparate data sources and formats.

Accurate and consistent master data is essential for a multitude of reasons. It serves as a standard reference point for different systems and workflows. Whether dealing with customer IDs or product SKUs, the data must not only be consistent but also represent the one true version of information. Anything below this standard can lead to operational inefficiencies, disparities, and costly mistakes.

We’ve all sat through multiple weekly review meetings where, rather than spending time figuring out the business strategy, the sanity of data itself was questioned. This situation meant that instead of making progress, teams agreed to circle back with authenticated data. The essence of the moment is lost, and rarely does the loop get closed until it is time for the next weekly review meeting. And the loop goes on and on.

The irony is that this is not an isolated issue, as several DTC and FMCG brands we've engaged with have shared similar sentiments and consequences of not having proper tooling or MDM.

While brands recognize the importance of MDM, many encounter significant challenges when attempting internal implementation. We collaborated with one of India’s leading DTC brands (referred to as SoTrue for confidentiality) facing hurdles beyond surface-level complexities.

Diverse data sources: As SoTrue operates on multiple online platforms, they receive data from various sources via different data delivery methods such as APIs, direct downloads, and email attachments. Initially, they used multiple tools to consolidate the disparate information, which proved challenging. So, they opted to build an in-house solution. However, lack of long-term amortization underscored the need for a more sustainable approach to data integration.

Maintaining masters of different entities: Handling data from different platforms proved intricate due to variations in formats, nomenclature, structure, and granularity. This diversity hindered SoTrue’s ability to accurately compute numbers and gain insights across platforms.

For instance, when attempting to analyze primary and secondary sales for a specific SKU, “Vanilla Energy bar - 32 g,” discrepancies arose. Zepto referred to it as “Vanilla Vegan Energy bar 32 gm,” while Blinkit simply labeled it as “Vegan energy bar 32 g.”

To tackle these disparities and standardize data for critical business entities, SoTrue initiated the need for master records for Customer, Product, Sales data, and Finance data. However, the execution did not unfold as planned. Instead of achieving a centralized and standardized system, each team began creating their own masters using Excels or Google Sheets, working in isolation.

Building translation layer: SoTrue had to create mappings to align data from different E-commerce or Q-commerce platforms with their internal processes. This included translating unique SKU formats and terminologies from platforms like Blinkit and Zepto to correlate sales with their own products. The mapping process typically managed by a category owner using spreadsheets, unfortunately, introduced several challenges:

Inconsistent data models: Different departments or teams develop their own data models, leading to inconsistencies in how data is defined and structured. This inconsistency results in confusion, misinterpretation, and difficulties in integrating data for analysis.

Data redundancy: In the absence of MDM, data redundancy and duplication occur. Duplicate records across different systems lead to inefficiencies, increased storage costs, and difficulties in maintaining data integrity.

Data staleness: The absence of a centralized solution heightened the risk of data becoming stale or outdated, impacting the relevance and reliability of insights generated from the data.

Maintaining data quality: Enforcing data quality checks on tools like Google Sheets became challenging, risking incorrect mappings and potential sales losses. Supply chains relying on flawed mappings could inadvertently deliver the wrong products, adding complexity to managing returns.

Data governance: Without a centralized system, there is no easy way to enforce any kind of policies on top of the data. Sensitive information, such as Personally Identifiable Information (PII) like customer phone addresses, remain inadequately protected. This exposes the organization to potential legal consequences and poses a threat to the brand's reputation, particularly in cases of identity leakage.

SoTrue was able to completely abstract its data management process by leveraging the GobbleCube platform.

GobbleCube took care of their entire data assimilation process across all marketplace platforms and internal tools. We even instituted the process of building competitor intelligence and integrating it with the sales data.

GobbleCube also managed all aspects of data cleaning, modeling, and triangulation to deliver insights in an intuitive and actionable manner, enabling SoTrue teams to take strategic and tactical decisions faster.

In our collaboration with SoTrue, the deployment of our MDM solution as part of the overall offering brought forth several key benefits:

If you are facing a similar challenge, then let's talk.