The average seniority of a data leader in an organization is only 17 months. There are few organizational functions where survivability is so low. The obvious question: why is it so hard for global data leaders to succeed?
In his keynote address at IRM UK last year, Roberto Maranca, now Vice President of Data Excellence at Schneider Electric and previously Chief Data Officer at Lloyds Banking Group and GE, explained very well that there are three main reasons why data teams fail.
First, the data is tribal. Within every organization, there are “tribes” of people who share beliefs, work practices and, most importantly, a dialect. This dialect inevitably colors the data they produce or manipulate. When this data leaves the tribe, it is often considered unfit for consumption by people not belonging to this same tribe.
Second, the data is an afterthought. Many projects depend on it in a very fundamental sense, but by the time people realize that the underlying data isn’t good enough, the budget is nearly exhausted, the deadline is approaching, and there are no more time to go back and fix things. The project therefore continues with decoded data.
Last (but not least!), the benefits of data are still very intangible. It is difficult to quantify the results produced by the work you do. While everyone agrees that “data is the new oil,” many data teams struggle to produce a solid business case.
The Product Data Business Case
Let’s focus on this last point in the area of product data in particular. How can a PIM executive create a rock-solid business case for their project and ensure buy-in and successful execution?
Conventional wisdom holds that the closer you are to the customer, the easier it is to quantify the benefits. It might be more accurate to say “the closer you are to the deal”.
The product data is indeed very close to the transaction. 81% of buyers research products online before making a decision, whether the product is subsequently purchased online or offline. A vast majority of purchases, and therefore – most of the organization’s revenue is heavily influenced by the content of the digital product.
This tells us that there is a strong business case for better product data. Now, how do we calculate it?
It is useful to think of two buckets of benefits.
First, let’s quantify the benefits associated with market reach. Here we can include:
- Faster time to market for products launched by your organization
This is a very tangible and easily quantifiable benefit. All you need to hit the $$$ figure is to understand how quickly you can start selling a new product and what that means in terms of lost revenue right now.
- Faster adoption of new routes to market
These may be new distribution or retail partners, new countries, new markets, new advertising opportunities. All of these require your product data, each in its preferred way and format, with specific customization requirements. Again, by determining how quickly you can get into a new sales channel, for example, you can calculate how quickly you can start generating revenue from that channel and what that means in absolute numbers.
What would it mean for your organization if you could support more sales and marketing channels at once (and keep introducing new ones) without creating a bottleneck in your data teams? Put an amount of $$$ on this one too.
Second, let’s look at the benefits associated with improving product data quality. The rationale goes like this:
- Excellence in PIM enables you to create and maintain up-to-date, rich and comprehensive content for each product.
- Excellence in product data syndication allows you to distribute this product content to all of your business partners and customers in their preferred format and method.
- As a result, your product listings are well-described, up-to-date, and attractive to shoppers – and earn a bigger share of organizational budgets and consumer wallets over competing products.
Now, quantifying this is a bit trickier than market reach benefits.
What you are looking for is an increase in discoverability and conversions for your products. What counts as a good boost varies across sales channels and product categories, of course.
You want to make industry-specific assumptions and then observe the results. Be aware that results will come with a delay, as it takes time for the product content you distribute to trickle down the value chain and present itself to buyers looking to make purchase decisions.
Good hypotheses will likely require segmentation by sales channel, as these will create a different context for your products from a buyer’s perspective. Thus, a different approach is needed to achieve a good fit with the context of each channel.
Hopefully, this gives you a head start in linking data excellence to tangible value for your organization.
Author: Alexandra Maximova: Sasha has been working on enterprise digital transformation initiatives for 5 years. At Productsup, she helps develop the company vision and best practices for the Product Content Syndication solution. She is passionate about all things manufacturing and will show you how to deploy your digital product data in the most impactful way possible.