Adaptive Contact Strategies in a Telephone Survey 

James Wagner * 

University of Michigan, Survey Research Center 

jameswag@isr.umich.edu 

Introduction 

An important step in the process of completing interviews using the telephone is establishing contact. There is a 

body of literature that focuses on methods for accomplishing this task in more efficient ways. Much of this literature 

focuses on determining the average best times to call; or the sequence of calls that have, on average, the highest 

contact rates. 

Greenberg and Stokes (1990) looked at methods that used the history of previous calls to determine the best time to 

place the next call. Brick et al. (1996) considered a similar approach that used logistic regression models to identify 

the best time of day, day of week and lag time between calls. Predictors in the model included contextual data as

well as information about the results of previous attempts. Others have looked at the impact of various sequences of 

calls (Weeks et al., 1980; Weeks et al., 1987; Cunningham et al., 2003). 

An alternative approach to this problem would be to develop household-specific estimates of the best time to attempt 

contacting each household. Such an estimate might provide the basis for a more efficient contact strategy. In 

addition, these estimates would allow us to “tailor” the contact strategy to the household. 

The problem is that for most households, we have very little data. Most cases are resolved within the first few calls. 

Most cases are called in one or a few time slots or “windows.” Very few cases are called in all call windows. In 

some -- or even most cases -- we will base our judgments about the best time to call on information from other 

households. This problem seems well-suited for multi-level modeling that allows us to borrow strength from other 

households when estimating parameters for households for which we have very little data. These estimates become 

more specific to the household as we gather data from that household. 

In this paper, I will outline such a strategy and present evidence of its efficacy from a randomized experiment. The 

strategy is described as “adaptive” since it learns from sequentially gathered data while also directing how those data 

are gathered. It develops an estimate of the current best calling time using the current call history data. The 

prescribed strategy is then attempted, the result is added to the data, and a new estimate is derived from this

supplemented data. Results from the field of reinforcement learning will be used to suggest future directions for this 

research. 

Background 

Efficient call-scheduling algorithms have long been a subject of research for survey methodologists. Weeks at al. 

(1980) looked at the best times to place a call using data from an in-person survey. This research was extended by 

Weeks et al. (1987) to a telephone survey and the timing of the first three calls was considered. Greenberg and 

Stokes (1990) employed a Markov Decision Process that used the history of previous calls to as well as the frame 

* Thanks to Richard Curtin for allowing this experiment to be conducted. Thanks also to Joe Matuzak, Dave 

Dybicki, and Rebecca McBee for working on implementing this experimental design. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

data to determine the best time to place the next call. The transition probabilities were estimated using logistic 

regression models. Brick et al. (1996) considered a similar approach that used logistic regression models to identify 

the best time of day, day of week and lag time between calls. Predictors in the model included contextual data as

well as information about the results of previous attempts. Other research has looked at calling patterns for the first 

few calls (Kulka et al., 1988; Massey et al., 1996; Cunningham et al., 2003). 

The concept I want to employ for exploring the best calling algorithm is a learning model. Over time, as we accrue 

more data on any particular household, we are learning about their patterns for being at-home and willing to answer 

the telephone. Successfully contacting a household at one time of day or day of the week (i.e. in a particular calling 

“window”) increases our estimate of the chance of success in that window for that household. Unsuccessful attempts 

to contact a household in another window decrease our estimate of the probability of achieving contact in that 

window. 

For many households, contact and interview results relatively early in the process -- within the first two or three 

calls. This means that for many households we have little or no data with which to estimate the probability of being 

home and willing to accept a telephone call within any window. For these households, we will need to “borrow 

strength” from the data generated by telephone calls to other households. This can be done using multi-level models 

(Gelman and Hill, 2007). In those households where we have no data, we are essentially using conditional means 

where we condition on the frame data that are available for all households (see below). 

Research in other fields has addressed similar problems in different contexts. In the area of marketing research, 

Rossi, McCulloch, and Allenby (1996) consider a similar problem. Their goal was to customize or tailor the face 

value of a coupon to a specific household. They attempted to estimate household-level parameters using 

demographic and purchase history data. They used multi-level models to do so. Bollapragada and Nair (2001) 

considered the problem of improving “right party contact” rates at credit card collection calling centers. They 

conceived of the problem in a manner similar to that of Rossi and his colleagues. In other words, their goal was to 

estimate contact probabilities for each household the call center is attempting to reach. They developed an algorithm 

that assigns the overall average contact rate to each household and adjusts these starting values for each household 

upward when a call attempt is successful and downward when the attempt fails. In both these papers, the authors

have attempted to estimate characteristics of households using historical data from households. 

In my application, I will also attempt to estimate household characteristics -- the probability of being contactable 

(i.e. at home and willing to answer a telephone call). I will use multi-level models where the household is a grouping 

factor. The fixed effects are frame variables available for all cases. The household-level estimates will be used to 

decide which cases have their highest probability of contact (not necessarily the highest of all cases) in the current 

window. Those cases those cases that have their highest probability of contact in the current window are prioritized 

in the current window. The models are re-estimated daily and the sample is re-prioritized at the beginning of each 

call window. 

Data and Methods 

The data come from an RDD telephone survey that is conducted on a monthly basis -- the Survey of Consumer 

Attitudes (SCA). The survey collects approximately 300 RDD interviews per month. The sample is prepared by a 

vendor that attaches contextual data to the sample file. The ZIP code of each telephone number is estimated using 

listed numbers from the same 100-bank. Census data for the associated ZIP Code Tabulation Area (ZCTA) are then 

attached to each telephone number. Table 1 lists several of the key context variables that are available. Of course, 

given the estimated geography of the case, any data that are reported for particular geographies can be attached to 

the sample in a similar manner. 

Previous research in suggests that the urbanicity and median income of the estimated geographic area (Dennis et al., 

1999; Brick et al., 1996) are predictive of contact rates. As part of the model fitting exercise, different 

transformations on some of these variables were tried. The natural logarithm of the median income sometimes

produced a better fit. Brick et al. (1996) reported using a similar strategy. Other research has reported that the 

proportion of the population that is Black, the proportion Hispanic, and the median years of education of the 

estimated geography of the telephone number are predictive of contact rates as well (Brick et al., 1996). 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Contact Propensity Predictor Variables (Xij) 

Context Variables 

Listed/Letter Sent 

% Exchange Listed 

Log(% Exchange Listed) 

Sin(% Exchange Listed) 

Total Households 

Household Density (households per 1000Sq ft.) 

Median Yrs Education 

Median Income 

Log(Median Income) 

Census Region 

% 18-24 

% 25-34 

% 35-44 

% 45-54 

% 55-64 

% 65+ 

% White 

% Black 

% Hispanic 

% Owner Occupied 

The data from the telephone survey include the records of every call. The time of each call (its window) and the 

result (contact/no contact) were recorded for each call. This initial set of calls was reduced for various reasons. The 

conceptual approach of the estimation is that each data element is an independent, random draw from a Bernoulli 

distribution of the probability that the household could be contacted. These “draws” allow us to estimate 

probabilities of contact within windows for each household. In order to make this assumption more plausible, any 

calls that were set as appointments were deleted. Since it is assumed that these were independent trials, the call 

number did not enter the models as a predictor. This assumption enables us to estimate the probability of being at 

home for any call in that window. Estimating the probability of being at home after eight calls of a particular 

sequence, for example, was not the goal. 

Table 2. Call Window Definitions 

Window Definition 

1 Sat-Sun-Mon 4pm-9pm 

2 Tues-Fri 5pm-9pm 

3 Sat-Sun 9am-4pm 

4 Mon 9am-4pm, Tues-Fri 9am-5pm 

In addition, for operational reasons related to the sample management software, refusal conversion and Spanish 

language calls were eliminated from the analysis. 

At the beginning of the field period, there are no call histories for the current sample. Therefore, we use data from 

prior months. Specifically we used the call records from the same month in the prior year (in order to capture any 

seasonal effects in the data) and the month prior to the current. Data from the current month are analyzed daily. The 

models are re-estimated daily, and the results are updated daily with call records from the first day through the prior 

day included. 

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific 

estimates of the probability of contact for each of the “call windows” (see Table 2). The predictor variables in this 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

i

model are the context variables described in Table 1. Let Xij denote a kj x 1 vector of demographic variables for the 

th th person and j call. The data records are calls. There may be zero, one, or multiple calls to a household in each 

window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is 

th th th denoted Rijl for the i person on the j call to the l window. Then for each of the four call windows denoted l, a 

separate model is fit where each household is assumed to have its own intercept which is from a N(0,

2

) distribution. 

The model is estimated: 

p 

Pr(R =1) = logit−1(β + β +β X )

il 0l 0il jl ijl 

j=1 

The next step is to compare the estimated contact probabilities within a household and find the window with the 

highest probability of contact for that household. During that window, the case -- along with all other cases that meet 

this criterion -- will be sorted to the top of list by the call scheduling algorithm. Each case had a window with the 

second highest estimated probability of contact. During that window, the case would be sorted on the list after the 

cases for which that window had the highest probability. In this way, all active cases were available for calling in 

every window. 

Under this sorting approach, a case with a low probability of contact could be sorted to the top of the list in any 

given call window, as long as the estimated probability of contact was highest for the case within that window. Once 

this group had been identified, they were sorted randomly. Future research aims to address what method of sorting 

within this group may work best. 

The experimental design required frequent sorting of the list as the call windows were specific to the time zone. For 

example, on a Tuesday, the list was sorted first thing in the morning, at 5pm EST, 6pm EST, 7pm EST, and at 8pm 

EST as the various time zones crossed the call window boundary. The experimental design required that the 

experimental and control groups be sorted in an interleaving fashion. The past practice had been to sort at the

beginning of the day. The control group sort was based on an algorithm that assigned weights to various factors and 

then sorted based on the sum of these weights. There were two weighting schemes. The first was used for the first

part of the month. It prioritized cases by time zone, those that had fewer than 5 calls, and those that were not called 

already on that day. Later in the month, the weighting scheme included the following factors: time zones, number of 

calls, whether the case had already been called that day, whether contact had previously been made with the number, 

and whether a household listing had been taken. 

Results 

The protocol appears to improve contact rates. Table 3 presents the overall contact rates for the experimental and 

control groups for the “eligible” calls by month and combined. The 

2 test reported here uses the Rao-Scott approach 

to account for the clustering of the observations within households. In addition to these differences, we found that 

approximately 30% of all calls in the experimental group resulted in a “Ring-no-answer” (RNA) while 39% of the 

calls in the control group produced this result. 

Table 3. Calls, Contacts, and Contact Rates by Experimental Group and Month 

Control Experiment 

Month Calls Contacts Contact Calls Contacts Contact 2 Pr> 

Rate Rate 

August 4,467 470 0.105 4,238 517 0.122 0.179 

September 5,418 507 0.094 5,025 596 0.119 0.016 

Combined 9,885 977 0.099 9,263 1,113 0.120 0.008 

Although contact rates and efficiency were improved by the experimental method, this method was not applied to 

refusal conversions or Spanish language cases. The efficiency gains for the calls governed by the algorithm were 

lost later in the process. This was because refusal conversions in the experimental group required more calls than 

those cases in the control group. Table 4 presents the total number of calls and interviews by those calls that were 

governed by the experimental method (“In-Algorithm”) and total calls. The inefficiency of refusal conversions in the 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Rate by Field Period Day and Experirrental Group 

0 . S 

0.4 

0. 3 

0 . 2 

0.1 

0 . 0 

10 15 20 25 30 35 

Day of Field Period 

group 

experimental group can be seen in total calls column. The total calls are nearly equal to those for the control group, 

despite the relative efficiency of the in-algorithm calls. 

Table 4. In-Algorithm and Total Calls and Interviews by Experimental Group and Month 

In-Algorithm Total 

Month Group Call s Interviews Calls Per Total Interviews Calls Per 

Interview Calls Interview 

August CON 4,467 84 53.2 6,510 150 43.4 

EXP 4,238 97 43.7 6,264 156 40.2 

September CON 5,418 96 56.4 6,961 146 47.7 

EXP 5,025 98 51.3 7,094 148 47.9 

Combined CON 9,885 180 54.9 13,471 296 45.5 

EXP 9,263 195 47.5 13,358 301 44.4 

This result seems to imply that there is some interaction between what happens in the early attempts at contact and 

the later phase of refusal conversion. It may be that the experimental method leads to contacts at times that are 

inconvenient for persons in the household. Future experiments will aim to control the refusal conversion calls to see 

if the efficiency of these calls can be improved. 

There were some other interesting results from the experiment. Figure 1 presents the contact rates for the 

experimental and control groups by day of the field period. The results for the experimental group relative to the 

control group appear to follow the trajectory of a learning model that explores the data. This exploration leads to 

early, relative inefficiencies, but then produces efficiency gains late. The trend line is a smoothed spline regression 

line. 

Figure 1. September Contact Rate by Field Period Day and Experimental Group 
 

 

 

 

 

 

 

 

 

 

 

 

contac t MEAN 

0. 1 8 

0. 1 7 

0. 1 6 

0. 1 5 

0. 1 4 

0. 1 3 

0. 1 2 

0. 1 1 

0. 1 0 

0.09 

0.08 

0 . 0 7 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 . 00 

CON EXP CON EXP CON EXP CON EXP group 

f- 1 --l f- 2 --l f- 4 -l n wi ndow 

Given that the experiment was defined using four call windows, it is useful to look at how the experiment and 

control groups fared over each of these call windows. Figure 2 presents these results for August. The experimental 

group had an edge in each of the windows. 

Figure 2. August Contact Rates by Experimental Group and Call Window. 

Conclusion 

The ability to establish contact with sampled telephone numbers is still an important part of the data collection 

process. Early evidence from an algorithm proposed here suggests that improvements in contact strategies can be 

made. These results may be limited by the particular survey context – a monthly survey with a fixed field period. 

The approach is adaptive in that it changes its proposed action after the results of prior actions are known. 

Future research will attempt to resolve the issue with the relative inefficiency of refusal conversions in the 

experimental group. In addition, more “exploratory” strategies will be attempted. In the field of reinforcement 

learning (Sutton and Barto, 1998), the balance between “exploration vs exploitation” is a key defining element of 

any particular learning strategy. For any given problem, the learning algorithm must decide whether to maximize its 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

expected gain from the current decision or to explore less than maximal options in order to see if there are 

suboptimal strategies for the current decision that lead to a higher payoff in the long run. I hope to extend the results 

of this experiment by employing more exploratory strategies that prioritize the cases about which we have more 

uncertainty to see if this increases efficiency or helps us to contact difficult cases. In addition, the method will be 

employed on other surveys to see if it is dependent on the context of the survey used here. 

References 

Bollapragada, S. and S. Nair (2001). Improving right party contact rates at outbound call centers. 

Brick, J. M., B. Allen, P. Cunningham, and D. Maklan (1996). Outcomes of a calling protocol in a telephone survey. 

Proceedings of the Survey Research Methods Section of the American Statistical Association, 142-149. 

Cunningham, P., D. Martin, and J. M. Brick (2003). An experiment in call scheduling. Proceedings of the Section on 

Survey Research Methods, American Statistical Association, 59-66. 

Dennis, J. M., C. Saulsberry, M. P. Battaglia, A.-S. Roden, D. C. Hoaglin, M. Frankel, N. A. Mathiowetz, P. J. 

Smith, and R. A. Wright (1999). Analysis of call patterns in a large random-digit-dialing survey: The national

immunization survey. Conference website of the International Conference on Survey Nonresponse 1999, 1-23. 

Gelman, A. and J. Hill (2007). Data analysis using regression and multi-level/hierarchical models. Cambridge ; 

New York: Cambridge University Press. 

Greenberg, B. S. and S. L. Stokes (1990). Developing an optimal call scheduling strategy for a telephone survey. 

Journal of Official Statistics 6 (4), 421-435. 

Kulka, R. A. and M. F. Weeks (1988). Toward the development of optimal calling protocols for telephone surveys: a

conditional probabilities approach. Journal of Official Statistics 4 (4), 319-332. 

Massey, J. T., C. Wolter, S. C. Wan, and K. Liu (1996). Optimum calling patterns for random digit dialed telephone 

surveys. Proceedings of the Section on Survey Research Methods, American Statistical Association, 485-490. 

Rossi, P. E., R. E. McCulloch, and G. M. Allenby (1996). The value of purchase history data in target marketing. 

Marketing Science 15 (4), 321-340. 

Sutton, R. S. and A. G. Barto (1998). Reinforcement Learning: An Introduction. Cambridge, Mass., MIT Press. 

Weeks, M. F., B. L. Jones, J. Folsom, R. E., and C. H. Benrud (1980). Optimal times to contact sample households. 

Public Opinion Quarterly 44 (1), 101-114. 

Weeks, M. F., R. A. Kulka, and S. A. Pierson (1987). Optimal call scheduling for a telephone survey. Public 

Opinion Quarterly 51 (4), 540-549.