There’s no question that customer experience (CX) is a data-driven discipline. After all, at the foundation of most CX programs are close-ended surveys designed to capture and analyze customer feedback.
Yes, metrics are a crucial element of every successful CX program. With metrics, you can establish a clear performance baseline and track trends based on actions you take over time.
But how do you know in advance if the actions you take will be the right ones? Wouldn’t it be great to have a crystal ball that lets you know exactly what to do to have the biggest impact on your anchor metrics?
Here’s some good news: You don’t need to have psychic powers to excel at CX. Instead, you need to use predictive analytics to clarify expected returns before you take every step—and to ensure you have clean data to power your CX metrics program. Only then can you take meaningful action based on your customer data.
In this blog, Richard Boehmcke shares how predictive analytics can benefit your business and therefore fundraising successes.
Big Data is not just the ability to store large amounts of data, more important is what we can do to the data in that large volume, how we use the data with such large volumes.
One of its uses is for data analysis needs. Big Data Analysis can be done in order to assist the decision making process (Decision Support) and strategy (Strategic Business) of an organization, business institution, or company.
Jeefri A. Moka explains more below.
Traditionally, enterprises have focused their data strategies around business intelligence (BI), but the rise of predictive and prescriptive analytics platforms, in part thanks to machine learning and artificial intelligence, is changing the equation. Even business intelligence itself is evolving, tipping in capabilities previously exclusive to business analytics platforms.
Analysts and consultants agree that understanding the distinctions between business intelligence and other analytics platforms, as well as the value each brings to the enterprise, matters significantly in getting your data strategy right.
Read the blog by Mary Pratt below on how business analytics is evolving.
Utilising data to make better business decisions is on the agenda for the majority of organisations, with almost three-quarters (74%) saying they want to be “data-driven,” according to a study by Forrester. However, data is only valuable if it transpires into meaningful actions – only 29% of organisations said their data efforts have led to actionable insights.
While there are many platforms out there that offer inbuilt analytics and insights, more often than not you’ll end up with a hefty amount of data – very little of which you can actually put to good use. To measure your data, analyse it and produce worthwhile, data-driven actions, you’ll need a team of experts to take the reins.
Generally speaking, there are three functions that fit under the data umbrella; collecting data, analysing it and producing actionable insights.
This blog by Noa Muratsubaki explains the differences between these three functions.
Five key figures in fundraising tech reflect on how technology has changed fundraising and what’s next. With contributions from Mike Gianoni (President and CEO, Blackbaud), Bill Strathmann (CEO, Network for Good), Mike Geiger, M.B.A., C.P.A. (President and CEO, Association of Fundraising Professionals), Steve Spinner (CEO, RevUp Software) and Jean-Paul Guilbault (President and CEO, Community Brands).
Ivan Wainewright acknowledges that buying a new CRM system or fundraising database is a daunting challenge for most charities and not-for-profit organisations, so he has written this book to feature issues that smaller not-for-profit organisations need to consider and be aware of.
This free book has been written for people whose day job is not the procurement or implementation of new databases, so it’s extremely helpful for any fundraiser thinking about stepping outside of their comfort zone.
Data governance is needed to ensure your organisation can consume data which has integrity and quality.
But how do you focus your efforts so that your governance programme can deliver the results needed?
Toochukwu Philip Ibegbu MBA shares with us how he was able to successfully launch data governance initiatives that made the most impact.
In this technical blog, Suresh Kumar Gorakala explains how to turn written comments into descriptive sentiment. This is extremely helpful when trying to categorise, segment and understand your audiences better.
This example focuses on Twitter comments, but this technique can be applied to any text field, including telephone call notes and emails.
Manually creating reports using Excel can be overwhelming to meet the organisational expectations for quality, insights, and velocity.
Many business intelligence tools exist: Tableau, Microsoft Power BI, Looker, Amazon QuickSight, Google Data Studio. However, moving from Excel to one of these tools can be more difficult than anticipated.
This blog by Thomas Spicer provides tips to help you create a methodology and a process that will help you find success with your new tool as you transition from Excel to a new analytics model.
Many smaller fundraising and non-profit teams can’t make the investment to fully utilise analytics.
In this blog, the great Peter Wylie uses data from two schools to demonstrate how to build a very simple predictive score using nothing but Excel.
If you’re struggling to adopt predictive analytics, this guide provides guidance on the initial steps to take to create nine segments based on capacity and propensity.
So, data analytics can help us to predict the future and find loads of people who will donate to our cause? Well, yes and no. But it’s a bit of a journey.
In this article, Thomas Maydon explains the four different types of data analytics:
Follow the link below for more:
This article by Becky Slack first appeared in the Guardian some time ago, but it’s still a great starting point for those interested in understanding how analytics can drive insight and aid decision making in non-profits.
Don’t be put off by data. It’s all about understanding your audiences. This guide contains some straight forward steps to get you started.
How can you tell a story with your data? Getting people interested in facts and getting them to take notice and understand their meaning can be a real artform. Here, Nayomi Chibana, shows a variety of visualisation techniques to inspire.
Joei Chan describes in detail how you can give your social media strategy an added boost with some simple data metrics.
What kind of skills does a data scientist need to possess? What kind of data scientist do you need? This is a really useful guide by Bob Hayes for those who know and don’t know much about data.
Having quality data is vital for non-profits to succeed, but there is more to it than simply having an accurate address. Simon Spyre highlights 7 ways to measure quality.
If you are looking for ways to engage with your audience via social media, Yoav Milner highlights five inspiring ideas in this blog post.