Data collection and analysis

The Driver Tree developed and agreed with BuddyHub as part of the Balanced Scorecard process - giving them an overview of their business and allowing a selection of an appropriate range of Key Performance Indicators.

The Approach

The current set-up for KPIs, and the company’s objectives, were reviewed. A Driver Tree was created (in collaboration with BuddyHub) to document the key objectives and their drivers. A Balanced Scorecard process was then used to select what should be measured.

The end-to-end process for matching Members with Buddies was captured with a series of workshops and step tables.

A rule based algorithm was developed to match Members and Buddies, incorporating aspects of machine learning and heatmaps, this incorporated: filtering and weighted features.

Software BuddyHub currently uses was compared based on features and cost via a literature search of previous comparisons (thus not being hampered by the biases of the author).

Example code was developed to calculate travel times, overcoming an existing issue with BuddyHub’s ‘Ops Board’. A review of this, and the general data set-up, was undertaken to create a data roadmap.

"Headlines are that we’re delighted with what the project has delivered and the volunteers have been an absolute pleasure to work with (both working above the expected time on the project)."

Catherine McClen, Founder and CEO.

The Client

Imagine a world where no one feels alone and anyone can make new friends. One million people over 65 feel lonely “all or most of the time” in the UK. Loneliness is harmful to both physical and mental health. It also raises the risk of developing dementia and heart disease, among other conditions. BuddyHub creates social circles for older people to help build better communities. BuddyHub is a Community Interest Community, which basically means they are here to benefit the public.

The Client's Problem

BuddyHub wanted to run their services in a more productive and efficient way. This required:

• Review of current processes, and data collection from Members and Buddies, and software.

• Development on how to analyse the data to extract key performance indicators (KPIs).

The Solution

• New KPIs agreed.

• Process introduced for identifying and calculating further KPIs.

• End-to-End process for matching Members and Buddies.

• Excel model prototype for matching Members and Buddies.

• Software comparisons.

• Example travel time code with roadmap for future data use.

The Benefits

• The KPIs allow BuddyHub to measure their own performance and adjust their approach to maximise performance in the future.

• The written process for matching Buddies allows BuddyHub to identify potential efficiencies, and provide a useful starting point for new recruits as BuddyHub scales up.

• The software comparisons identified a method for doing relatively simple and quick software comparisons. Using the Capterra and Getapp websites, initial analysis indicated that Eazipay could be replaced by GoCardless or other software such as Stripe.

• The prototype matching algorithm will allow BuddyHub to more efficiently match Members and Buddies saving time in the matching process.

• The travel time calculator showed BuddyHub that complex software solutions are not needed, instead some simple features of open source software can provide effective solutions.