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Is it time to give your product data a good going over?

Product data cleansing – why (and when) you need to do it.

Product data and attributes include the selling words and names that describe your products to your sales team and (more importantly) your customers. 

If you have products listed in your Point Of Sale (POS) system, on your website, on a reseller's website, in your accounting system (eg MYOB, Xero, ERP), in a price list (spreadsheet, CRM), in brochures, on an ecommerce channel like Facebook market, Amazon, Google shopping, or in a Product Information Management (PIM) system (or about to go into a PIM)... you have “product data”. 

There's a good chance you receive the details about each product attribute from a wide variety of sources such as the supplier, your own staff (marketing and sales team, accounting team, business development), customer feedback, reviews, resellers…

Over time, the product data you have collated can become untidy, inconsistent, inaccurate or unwieldy. Which is why it is then time for a product data cleanse. 

Data cleansing

Data cleansing is a process where you remove product data that does not belong in your dataset and replace it with data that does belong!

You may need to clean your product data for a number of reasons:
  • Your existing data is poorly maintained and unreliable, with inconsistencies, errors, duplicates, missing information and the wrong information stored in the wrong locations
  • Maintaining product information across multiple systems is time consuming and requires complex processes to achieve, so is performed infrequently or inconsistently
  • Different systems have inconsistent data about the same products
  • There is no single source of truth about your products or the data is so unreliable that staff maintain and use their own product lists rather than rely on the company ones
  • Sales personnel and customers frequently chose the wrong products because the information is incorrect or incomplete

Correcting, completing, and standardising product attributes is vital to successful selling and providing a rewarding customer experience. It saves time for the business and reduces errors and rework.

Four critical areas

Here are the four areas Ascend 7 thinks are critical to focus on when cleaning your product data:

  1. Standardisation
  2. Completeness
  3. Accuracy
  4. Usability

Standardisation

All product attributes like brand, price, colour, currency, units etc should be uniform and in the same format.

For example:

Currency before cleansingCurrency after cleansing
$A$
A$A$
AUDA$
<empty>A$

Standardised also means that the right data is in the right columns!


Completeness

It’s so obvious but make sure you’re not missing any key product attributes.

Units of Measure before cleansingUnits of Measure after cleansing
EAEach
EachEach
Llitre
<empty>Each

The different systems using your product data will require different attributes.  Make sure you understand these different requirements and ensure your central single source of truth handles these correctly.  

Accuracy

Check for new details or mistakes (like transposition of details in the wrong place).  Clean data is correct and up to date.

Before cleansingAfter cleansing
TitleBrandColourTitleBrandColour
Acme Blue WidgetAcmeBlueAcme Blue WidgetAcmeBlue
Acme Green WidgetAcmeBlueAcme Green WidgetAcmeGreen
Acme Black WidgetAcmeBlueAcme Black WidgetAcmeBlack
WidgCo Black WidgetAcmeBlueWidgCo Black WidgetWidgCoBlack

We often see units of measure that are badly wrong. Not as big a problem in a POS where the customers can’t see that the measure is in litres when it should be millilitres but can cause customer, production, ordering or even legal issues if the data is used in other systems.

Usability

The product information must be readable and usable by everyone who needs to access it.  

Make titles direct and concise.  If you use abbreviations, ensure they are consistent and commonly understood.  Remove any HTML, extra white spaces, duplicate words, unnecessary capital letters; check spelling and grammar; add in the brand name… anything so it makes sense to the user, whether that is your customer, resellers, sales staff, accounts staff etc.

Title before data cleansingTitle after data cleansing
BLK halter dressBlack halter dress
<b>Plus size</b> halter dress in balckBlack plus size halter dress
BL MIDI dress!Blue midi dress

See this blog post from Sales Layer for more information about product descriptions: https://blog.saleslayer.com/tips-for-good-product-descriptions


Reusable product data

Our recommendation is to ensure each product attribute is also suitable for inclusion across multiple systems – your ecommerce site, your accounting system, your CRM, the product information you provide to your resellers and distributors. The fewer times you have to enter new data for each product attribute the better, so when you are writing a new attribute, consider how it will appear in the other systems your business has. This will ensure your product data remains standardised, complete, accurate, and coherent.

Giving your product data attributes a freshen up will get you off to a fantastic start for 2022. Aim for consistency and standardisation.

Talk more to us about product data cleansing

Or read more in our Guide To Product Naming Conventions.