Introduction
Where do the real results and action from a corporate conference come — from the energy and ideas sparked during the meeting, or on the plane? It's the event that galvanises the business, yet companies put far less focus into measuring and costing meetings and events than travel. That's why I am so interested in something promising to transform our industry — predictive analytics. It is admittedly still at an early stage, but organisations of all sizes can benefit.
The terms predictive and behavioural analytics, AI, machine learning and big data may generate the headlines but it is important to be clear about what they really mean:
- AI = using software to analyse data - Machine learning = software created to learn from data, adapt accordingly and improve - Big data = a collection of vast amounts of data that is then analysed by people or AI
Predictive analytics is the practice of using artificial intelligence (AI) and machine learning to forecast future events. This typically means studying past data and using algorithms to detect meaningful patterns which can suggest what is likely to happen — then taking different action as a result.
AI machine learning is powerful because it analyses data, makes assumptions, learns and offers predictions at a scale and depth of detail impossible for human analysts. How can business reap the benefits? Large companies will be able to use data culled from their employees' history of attending meetings and events, while smaller businesses will use sector-wide data.
How predictive analysis can benefit the meetings and events industry
Using historical data makes it possible to detect behaviour patterns and trends to drive smarter buying decisions. AI aims to learn from our previous behaviour to serve up what we want now. For example, using AI we can learn the average meetings spend per delegate within a large organisation or even an industry sector. We can view the data in multiple ways, such as spend per event or spend per quarter.
As we correlate more data and use AI to analyse deeper, we can look at the total cost per delegate for one event or time period — including travel, food, credit card expenses, time out of office, etc.
This data can help a buyer benchmark their meetings spend against industry or company trends and therefore inform their choices: they can evaluate whether a venue is relatively expensive or a good deal, and they can negotiate with suppliers from a stronger position.
This can lead to what is called a predict-and-prevent approach. Let's look at an example that combines AI and the psychology of persuasion:
James from the Innovation unit in Edinburgh wants to hold his department's annual three-day strategy summit at a hotel offering a discount if delegates stay the night. Thanks to AI, his company's booking team can advise him:
"We thought you'd be interested to know that nine out of ten department heads choose a similar deal at a neighbouring hotel and paid an average of 25% less (a £250 saving) per delegate by choosing this preferred supplier. Thank you for helping us get a better deal."
Following the psychological trait of wanting to fit in with the social norm, James opts for the preferred venue.
Tools to help
It's essential that corporates consult a meetings and events management company with technology specific to the industry. Then organisations can benefit from tools that use analytics to
1 Calculate demand For example, 100 delegates are invited to an event and past data reveals that only 22% will RSVP within a month but 58% more will finally accept (many after the booking deadline). You can use the one-month figure to predict how many will actually attend and thus book the right venue at the right cost well ahead of the event.
2 Calculate the most cost-effective and time-efficient location and venue for your company to hold a meeting, for example when delegates are coming from three specific countries.
3 Advise the business For example, to send fewer people to a conference in Asia-Pacific if airfares to the destination are predicted to rise at the time of year that the meeting is scheduled.
Machine learning about your organisation's booking habits can play a vital part in enhancing delegate experience, a key issue in today's meetings and events industry. An example is an employee heading to a conference in Barcelona. They could be overwhelmed by looking up hotel recommendations on a consumer website, but with predictive analytics the booking system knows you and your business. It can recommend hotels
- where colleagues stay - ranked in order of distance from the conference venue - based on employees' individual preferences
Expenses management
AI can streamline the time-consuming task of verifying delegate expenses and instead reimburse them automatically … and this is not the invitation to spend, spend, spend that it might seem. Apps that speedily analyse spend patterns and individual behaviours to detect anomalies are a godsend to managers. They can identify the 5% of delegates (an industry estimate) who make bogus claims without aggravating the 70% who never cheat or the 25% who make the occasional mistake. Some tools carry out more intelligent analysis, so you don't penalise the delegate who spends an unauthorised £10 on an in-room movie but skips a £30 dinner as a result.
Challenges
- Most companies have a large volume of unstructured data housed with multiple online services and offline providers;
- Policy might vary according to market. For example, what's good in Japan where luxury equals success, and is therefore a good investment, might not be suitable in the US;
- Predictive analytics need to be integrated with your travel platform if you are to exploit opportunities for better deals across your organisation.
Advantages
Solving these challenges is where a meetings and events management company with plentiful crossmarket data comes in. With a full, 360° picture you can identify where even small changes to booking policies and supplier programmes can generate savings.
You will be able to control costs, ensure your spending caps suit the market, choose alternative suppliers or even suggest that meetings organisers reschedule an event if prices at a specific destination are forecast to be steep.
And buyers have the added advantage of knowing they are helping business leaders with budget planning and intelligence on future costs.
Conclusion
There are a couple of caveats. First, only timely data can drive impactful action so the latest information must be accessible in hours not weeks. Second, this is a social as much as a technological shift … and are we ready to do what the machine says?