Modelling demand and performance in HMRC call centres

1. Modelling demand and performance in HMRC call centres Steve O’Donnell Personal Taxes Operations – Contact Centres 03000 573701 steve.o’donnell@hmrc.gsi.gov.uk

2. Revenue 'missed 44 million calls' HM Revenue and Customs 'missed 44 million calls' BBC news web-site 15 January 2010

3. Percent call attempts answered • Is the percentage ratio of calls handled to all call attempts • It is the primary statistic HMRC uses to manage its contact centres over the medium to long term • It is a sensitive indicator of how easy it is for the customer to access HMRC call centres • It is easily understood by Parliamentary Select Committees, the Media, and Revenue senior management • It is important to our customers and to HMRC’s reputation • In 2010-11 HMRC only handled 48% of call attempts

4. Managing call centre performance resource demand • a queuing theory problem originally addressed by Erlang • theory now implemented in commercial packages used by call centres • but typically planning requires detailed forecasts of demand and resource at half-hourly intervals into the future • Not particularly suited to medium to long term planning and “what-ifs” • Doesn’t give percent call attempts handled

5. HMRC’s inhouse performance model INPUTS OUTPUTS Demand (calls offered) Percent call attempts handled Average call handle time Staff resource • Not based on queuing theory – based on historic correlations between inputs and outputs • Worked on a weekly basis • Suitable for medium to long term planning and what-ifs • But….

6. 1.0 • demand is imperfectly correlated with percent call attempts handled • The in-house model only gives an approximate answer • Could it be improved? 1.0

7. How do you measure demand in a call centre? • The “industry standard” is calls offered • When a call centre is under pressure customers who fail to get through tend to redial • There are always some customers who need to make multiple contacts to resolve their issue • The two factors above are confounded and make it difficult to identify what the real or “underlying” demand is faced by contact centres under pressure

8. A model of demands Suppose in a period there is a set of (customers with) demands. Suppose there is a given probability p, in that period that a call attempt will be successful. Suppose for each demand successive call attempts are made until either a call attempt is successful (the caller gets to speak to an adviser) or after some set number of call attempts, x (= persistence) the caller gives up and the demand is unhandled. Then it can be shown….

9. The demand equation Where q = 1-p = probability that a call attempt isn’t answered x = persistence – the number of times a caller will make a call attempt relating to a demand before they give up

10. Properties of the demand equation • It doesn’t seem to be in the literature • In simulations with variable q and x it gives reasonable answers until p= 1-q <0.2 • q and x can be estimated from HMRC data • When applied to HMRC data it always gives extremely reasonable looking results

11. Demand is always > calls handled Demand is always < call attempts Demand is always > number of callers

12. Properties of the demand equation II • the expression relates demand to q the probability that a call attempt isn’t handled = 1-probability that a call attempt is handled • The probability that a call attempt is handled is the same as the percent call attempts handled What’s the relationship like between underlying demand and percent call attempts handled?

13. 1.0 • demand (calls offered) is only moderately correlated with percent call attempts handled • The in-house model only gives an approximate answer • Could it be improved? 1.0

14. 1.0 1.0

15. I found… • For almost all our helplines the ratio calls handled/underlying demand was very highly correlated with percent call attempts handled • The relationship varied slightly from helpline to helpline • A power curve generally gave a very good fit to historic data for each helpline • Which implied… • If we changed to using my demand metric we could re-engineer the in-house performance model to make it highly accurate at modelling percent call attempts handled

17. What’s happened? • Got the green-light to introduce my demand metric and revised model for 2011-12 planning • Most successful tax credits renewal cycle ever – 70% of call attempts answered (35% the previous year) • We went from 48% call attempts handled in 10-11 to 74% call attempts handled in 2011-12 • The model was used to work out how we can get to 90% call attempts handled in 2014-15. £34m reprioritization on contact centre investment within HMRC recently agreed directly as a result of the model. 1000 jobs created. Accurate modelling makes convincing financial cases • The overall theory has been elaborated to cover demand which needs advisers and demand which can be handled automatically, and other things… • We still have bad days and bad weeks sometimes • Erlang great for short term planning – the approach here has proved highly effective for medium to long term contact centre planning

18. Thank you HM Revenue & Customs Steve O’Donnell Abbey House, Telford TF2 9RG Telephone no. 03000 573701 steve.o’donnell@hmrc.gsi.gov.uk