[Infographic] Solving Retail’s Big Data Challenge

Solving Retail’s Big Data Challenge

Retail is growing - the US Census pegs traditional retail growth at 4% per quarter and e-commerce at 15% per quarter. But there’s a channel that’s showing outsize growth: Google Shopping is hitting more than 50% revenue growth and controls half of all retailer clicks in the channel

(This is just one of the important e-commerce channels we’re actively investing in and developing for - stay tuned for more updates in the near future.)

But while Google Shopping represents an incredibly powerful new opportunity for retailers, it’s impossible to succeed without conquering its biggest challenge: data.

Retailers like you sell thousands, if not millions, of individual stock-keeping units (SKUs), each with its own set of attributes (color, material, size, pricing). This is an incredible overflow of data points that could be pointing you in the direction of higher sales and fatter margins. 

...If you could get at the insights they offer. Which high-margin products make the most sense to put your budget behind, and by how much? How can you make sure you’re deploying your budget to convert every potential sale at the most efficient spend level?

This is why we created QuanticMind Shopping - a new solution to a very complicated challenge. QuanticMind Shopping uses the power of data science combined with machine learning to parse the enormous amounts of data that retailers grapple with each day, pulling the most important insights from the most relevant data to deliver better results every time.

Here’s how it works. For more details on how the power of big data drives better performance for retailers just like you, visit our Retail resources section.

Solving Retail's Big Data Challenge

 

 

 

QuanticMind:

What is the state of online retail?

Retail is growing. Projected spend by 2020 is... $630,000,000,000

Where's the biggest opportunity?

There's one particular retail channel showing massive growth:

Google Shopping:
- 50% quarter-on-quarter growth
- ...and controls 50% of all clicks

E-commerce:
- 15% quarter-on-quarter growth

Traditional retail:
- 4% quarter-on-quarter growth

How do you win at Google Shopping?

You need to conquer both halves of the big data challenge:

Data Overload:
- Too much data in too many places, no way to leverage actionable insights

Data Scarcity:
- No historical data available for new products to realistically plan budgets

You need to adapt to changing data and markets:

  • 231% increase in PLA Black Friday spend from 2015-2016
  • 105,840 data points - 210 DMAs x 7 days x 24 hrs x 3 devices = total bid variations for every SKU
  • 50+ product attributes for every product you sell

How QuanticMind solves the big data challenge:

  1. Collect, connect and leverage all data for perfect execution
  2. Diagnose problems and rapidly surface insights to adapt to market changes
  3. No more broken titles or costly errors...no more money down the drain

How we help retail marketers like you:

  • Leading Automotive Retailer: +26% revenue
  • Leading Food and Beverage Retailer: +10% ROAS
  • Leading Specialty Retailer: +24% margin

About the Author

Andrew Park

Andrew Park is a content marketing manager at QuanticMind. A UC Berkeley graduate and lifelong Bay Area resident, Andrew has done tours of duty in editorial, PR and marketing, and now works with the QuanticMind team to communicate the importance of data science and machine learning in digital advertising.

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