The sports apparel industry is one of the fastest-rising and most competitive industries in the world. The sports apparel market is predicted to register a CAGR of 5.1% in the period 2019-2026 and is estimated to grow to $248.1 billion by 2026. You have to be creative with advertising dollar spending when you are competing with mammoths like Nike and Adidas. One secret weapon you can use is data.
Data has a huge potential in the sports apparel industry. The best sports apparel brands have their own data experts, who analyze information and create strategies to boost sales, increase revenue and build brand loyalty. Thereby, recent years have seen a demand for data analysis in the sports apparel industry.
How do Advertisements work?
Ads are one of the fastest ways to gain visibility to your target audience. Many companies, including your competition, take advantage of this. Companies leverage web scraping to keep track of the most common ads their competitors create. This data is then used to identify new trends, build new campaigns, bring changes in ad-spend budgets, gain insight into your target audience, and much more. Insights like these help companies achieve strategic advantage and boost their own advertising.
Brands spend money on digital advertisements using bidding. When you set your bid, you're telling the Ads platform the maximum amount you're willing to pay for a click/impression on your ad. The timing of when you run your advertisements is a huge deciding factor in determining the conversion rate.
But how do you make sure that the timing is optimal for maximum ROI on advertisements?
That is the answer our customer was looking for.
Customer
A US-based leading sports brand was looking to devise a marketing strategy to eliminate excessive Advertisement expenditure. They wanted to monitor five of their major competitors across multiple marketplaces across US / ASIA / Middle East and EUROPE.
This would help them gain insight into their competitors’ marketing strategies and identify trends in their behavior that could be used by the client in order to avoid spending money on advertisements that may not yield results.
Challenges
The sports brand was witnessing a budget overshoot due to excessive advertising. The brand had been rising steeply, but so did its cost of marketing.
With an exponential rise in Ad spend coupled with a lack of adequate data, the brand’s marketing team found it difficult to make informed decisions about where to invest in digital channels. The team also lacked insight into how effective their investments were at driving sales, customer acquisition, and retention on multiple platforms like Google Search, YouTube, Facebook Ads, and more.
When they approached Datahut, they highlighted the following challenges:
The brand did not have competitive data to understand where and how it could save money. This data was spread across hundreds of online channels.
Their in-house analytics team did not have the resources to gather a massive amount of accurate competitor data.
The self-service tools they used had multiple problems, from coverage to quality.
They wanted the data from websites like Amazon, Walmart, etc., every three hours.
It can’t take more than 90 minutes from data extraction to pushing the data into the analytics tool the Brand used.
Here are questions about competitors our data answered for the customer.
How many products are out of stock in each marketplace/website?
What products are being advertised in online marketplaces?
What are the rankings for the products for a set of keywords?
Are the prices/promotions changing? If yes, by how much?
How Datahut helped
The company turned to web scraping software for help and found out how this helped them save millions on Ad spend.
Our role in building the solution was to get the data for performing the analysis as soon as possible. The data scientists at the customer's company and the revenue management team wanted to get their hands on the data as soon as possible.
We extracted competition data from over 50 websites and pushed it into a database for the brand’s analytics team to work on. This data was refreshed automatically by our platform every three hours.
To keep up with the schedule - we scaled our servers and increased the speed of data extraction. We passed the extracted data through our Quality assurance tool, and the data was delivered within 1 hour.
Thereafter, we built a connector to help the sports brand’s business teams connect their proprietary BI tool with our latest data. This BI tool checked for anomalies and reported, if any, to the marketing team.
As a result, the brand was able to eliminate its manual data collection process and reduced the time to get the advertising intelligence from days to just a few minutes. Upon analysis, they found opportunities to optimize ad spend faster and bring in major changes in their marketing campaigns.
Results
The Sports Apparel brand saw a 7.2% improvement in ROI within 90 days of using Datahut.
With Datahut, the sports brand was able to get better visibility into what happens in the market and when; this helped them reduce wastage on advertisement expenditure by millions of dollars, improve ROI by 7.2% on the marketing campaign spend, and get a quicker response time to an opportunity from days to minutes.
Datahut also helped them optimize their revenue management team's efforts by providing them with better insights into when to start advertising, campaign, and stop it based on actionable competition data.
In just 90 days of using Datahut, the sports brand:
Eliminated wastage on advertisement expenditure by millions of dollars.
Improved ROI by 7.2% on the marketing campaign spend.
Reduced average response time to an opportunity from days to minutes.
They had better insights on when to start advertising, campaign, and when to stop it - based on actionable competition data.
The revenue management team got better visibility into what happens in the market, especially on days like black Friday when things go mad from crazy.
Future Plans
Witnessing positive results over just 90 days, the sports brand plans to work with Datahut to further:
Scale to more channels (100 sources) within the next 6 months.
Reduce the frequency of data refreshes from 3 hours to 1 hour.
Add a new feature to monitor search performance for a set of keywords and observe how it impacts the ad spend.
What it means for you
If you’re working for a brand that needs to find growth/optimization opportunities - competitors' data can help you develop creative strategies. Contact Datahut to learn more.