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Case Studies
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Case Studies

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Just take a look at what our clients say about us.

Introduction

Showcase are a company who have been providing the music & entertainment industries with a very thorough and accurate directory for over 50 years. In a project to reduce manual work and expand their directory, they approached us to provide them with data.

We'll let James Stanbridge, Director of Showcase, explain...

James says

Hi! We’re Showcase Music; the world’s leading online music directory and the publisher of ‘International Showcase’, an annual publication considered the ‘bible’ by the music production industry.

The challenge

To celebrate our 51st successful year we were exploring new ways to grow our business across the music and entertainment industry and decided to increase our directory offering to include Artists, Agents, Bands, Booking Companies together with offering some pertinent industry metrics.

The problem

Without an available database to buy, where do you start? Buy an existing company? Undertake an expensive telephone marketing exercise? Spend thousands of hours manually searching the internet and writing down contact details? All of these were cost and time prohibitive.So, how do you get specific, good quality data in volume?

The solution

Having spent hours speaking to various database companies I was eventually introduced to Chattering Monkey, a refreshingly clever group of data specialists who suggested we could ‘scrape’ publicly available data directly, using their ‘scraper-bots’ and keep it enriched through automatic updates. This was a much faster and much more cost effective solution than those offered by others, where do I sign?

After an exciting meeting where we discussed how the data was to be used and also other possibilities for its future use and re-use (great brainstorming session!), we agreed the data we wanted to collect and set some milestones.

Due to the volumes of available data relating to music artists and bands it was important to apply ‘filters’ within the search algorithms to ensure we only collected the right levels of data e.g. the artist/bands had over ‘X’ number of followers, shares, likes etc. whilst being mindful of the amount and type of data we wanted to use.

We also had to ensure other parameters were included, such as those to check that we’re only capturing data from the most trusted web sources verses the most recently published content to maintain high levels of accuracy and relevancefor its ongoingenrichment.

We agreed Chattering Monkey would create a custom ‘scraper-bot’ for each website and using their scraper platform, we set them to run daily, collecting and returningdata to us.

The results

After a quick two weeks of development the bots were ready to launch. We started to see data being returned in minutes and after a couple of ‘tweaks’ were collecting 90,000 records every two days, with 360,000 records collected in just two weeks. The data is of excellent quality and not only is it kept up to date automatically, but, by enriching it with new contacts as they’re identified and tracking their maturity, I can alert my sales team to make early introductions and increase sales too!

Who are Horsecode?

Horsecode are a company specialising in the analysis of horse racing. Previously they have worked with traditional forms of analysis (using knowledge of previous form etc.) and have provided tips and betting advice to their customers and investors. They started exploring a statistical analysis/machine learning approach given the current accessibility of computing power and data.

The issue

Horsecode were populating their database of racing results by importing data manually entered into spreadsheets every day. The manual task led to a high number of errors in the data which was negatively affecting their analysis. It was time consuming and expensive to enter records so their data set was not as comprehensive as they would have liked. Improving the accuracy and therefore yield of their algorithms was key to their future business strategy. Doing this relied upon increasing the accuracy and size of their data set.

They found a racing results database product that used an inappropriate technology which was neither efficient nor quick. The format of the data meant a large amount of work would be needed to translate it into the format they were working with. They approached other companies that held this data in a more desirable format, but the costs were prohibitive.

What they needed was a way to increase the accuracy and size of their data set that they could control.

Why us?

Having worked closely with one of the guys at Horsecode a few years previously, Simon (our CEO) was approached to see if he could help. With 15 years’ experience working in a variety of industries as a systems designer, and his love of a challenge, Simon was only too keen to get involved.

The solution

Due to the financial and time cost associated with changing existing analysis systems, the interface of the database could not change. The analysis of the existing database structure and the identification of the full data set available on various racing results sites led us to design a new database. The existing one would not cope with the volume of data we’d be storing in the future and would become a serious bottleneck. The new database was designed for high speed queries. We maintained the same interface as the old database; no changes were required to existing analysis systems.

The first goal was to correct the existing data. We created a process that used their historic spreadsheets and verified every record with data found online. Records were corrected, cleaned and normalised before being imported into the new database. Every spreadsheet generated in this interim period was imported via the new process.

The main goal was to provide a database of accurate data, containing as many records as were available. We identified three sites with good data which we decided to target. Creating and integrating custom scrapers for each site with Chattering Monkey’s platform, we set them to run every day. To build up the database quickly, we created a spidering scraping strategy which pulled up to 90,000 race results per day using the previous day’s results as a starting point. We build a decent historic data set going back around 15 years relatively quickly. Using three sites enabled us to sanity check the data against each other to protect against errors. against each other to protect against errors.

THE results

While sanitising and correcting the spreadsheet data, we discovered a 1.5% error rate in the existing manual process. The benchmark error rate for manual data entry is 1%. Once the manual element of the process was removed, this error rate dropped close to 0%.

As the database grew to over 1,000,000 race results, Horsecode saw a huge increase in the accuracy of their predictions leading to greater external interest in their algorithms. The optimised, cloud hosted database has increased the speed of their analysis, lowering the cost. They have also launched a paid service giving access to the data to interested parties; a new revenue stream not envisaged at the start of the project.

Chattering Monkey have continued to provide Horsecode with results data every day for the past two years. Due to the success of this initial project, we have been working very closely on scraping further related data and some very exciting machine learning analysis which we'll write about in the future.

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