This paper uses transaction-level data from a firm in the High Technology sector to compare the revenue garnered from customers who purchase via sales representatives to that garnered from customers who purchase online. The interaction mode may be correlated with the customer’s unobserved valuation for the service, and this potential endogeneity is addressed using an instrumental variable. Specifically, the data indicate whether the customer’s details were recorded in the company’s information systems following an initial contact, such as the download of a free version from the company’s website. The availability of such leads facilitated representatives’ ability to contact customers, generating exogenous variation in the probability of interacting with a representative. Instrumental variable estimates indicate that such interaction lowers the total revenue garnered from a paying customer during the sample period. The primary driver of this result is a churn effect: customers who purchased online displayed a longer overall relationship with the firm, and were more likely to renew an expiring subscription. An alternative explanation, according to which representatives lowered revenues by reassuring customers regarding the adequacy of cheaper versions of the product, is not supported by the data.
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Enterprise customers are handled via a specialized sales team, and tend to purchase rather expensive, sometimes tailor-made versions of the firm’s product. Of note, despite the labeling of the online channel as targeting SMB customers, in practice there was no effective mechanism that limited the access of large customers to online purchasing via this channel.
Neslin and Shankar 2009 provide a survey of the empirical literature on this issue. Specifically, Hitt and Frei (2002) and Campbell and Frei (2006) demonstrate stronger client retention in online channels in the banking sector. Danaher et al. (2003) find that brand loyalty in the retail context is strong (weak) when shopping online for products with large (small) market shares. Again in the retail sector, Wallace et al. (2004) find that multichannel shopping increases loyalty. On the other hand, Wright (2002) survey of the impact of online banking cites Beckett et al. (2000) who conclude that it may have pushed customers out of a passive loyalty mode. Ansari et al. (2008) also find that internet usage may reduce loyalty in the financial services sector.
I am grateful to a referee for pointing out this possibility.
They also note that the majority of papers in this literature either do not correct for self-selection, or do so via matching techniques.
A caveat is that, if a customer conversed with a sales team, but did not complete a transaction, and later performed the transaction online creating a new ID, the analysis is likely to miss the fact that interaction with a representative took place. The data do not provide relevant information on this issue, and it is also difficult to gain a qualitative insight into its prevalence.
The data contain very few transactions that cannot be characterized along these two dimensions: six transactions are missing the plan description information, and an additional 263 transactions belong in two other small plans.
Since the transaction time within the day is not recorded, the bounds of (3/31)*99 and (5/31)*99 were used in such cases. The 92 cases alluded to above are those in which the discount fell within the bounds.
These additional regressions are available from the author upon request.
The data also report a “latest marketing” variable which is typically, but not always, identical to the “first marketing” variable that is used in the analysis and discussed above.
A caveat is that some of these customers may have performed their first transaction before the sample period. Notwithstanding this issue, it does not change the point of this analysis: that the technical issue that led to missing channels became less relevant over time.
The need for this manual entry stemmed from lack of synchronization of some of the firm’s information systems.
Such customers may have visited the website, but the visit did not result in a recorded marketing channel that was then associated with their account.
As described above, customers whose first contact with the website took place in January or February of 2012 were much less likely to have their marketing channel recorded than customers whose first transaction occurred in March or the later months. These minor differences in the timing of the first contact are not likely to be correlated with underlying preferences.
In transactions involving multiple months, it is possible that payment is received in installments over time, and some of these payments could, therefore, be received by the firm only after the sample period. For simplicity, I ignore this issue and consider the total amount of transactions signed up for during the sample period.
Some additional regressions below are conducted at the level of the individual transaction. In those regressions, it is understood that the variable W i r e is defined at that level as well.
A referee suggested that using the log of revenues, rather than their level, as the dependent variable will reduce the impact of large transactions. Table 16 in the appendix presents such results, in a manner parallel to Tables 7 and 8 above. As can be seen there, the results using logs are qualitatively very similar to those presented above.
Note that we cannot tell with certainty whether the customer’s first observed transaction is really her first transaction—this would not be the case had the customer performed a previous purchase prior to the observed sample period. Despite this limitation, restricting the sample in this way makes it possible to address the question of interest.
Recall the discussion of this issue in Section 2, where it is noted that, in such cases, customers are reimbursed for the unused portion of the subscription.
When performing regressions at the level of an individual transaction, it is not possible to include a customer fixed effect since the interaction variable does not vary within customer, except in the case of a small number (35) of customers, as discussed in Section 2 above. For this reason, among others, the paper focuses on measures (revenue, duration) that characterize the entire relationship with the customer, and not on transaction-level analyses.
As above, results using combinations of this indicator with indicators for the Direct and Organic marketing channels yield qualitatively similar results.
The same message arises from unreported regressions that perform the same analysis at the single transaction level, available from the author upon request.
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The author is grateful to a company in the High Technology sector for making their data available, and for their helpful feedback. Itai Ater and Saul Lach provided helpful comments. Orry Kaz provided excellent research assistance. This research was supported by the ISRAEL SCIENCE FOUNDATION (grant No. 1338/13).
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Eizenberg, A. Estimating the impact of interacting with sales representatives on customer-specific revenue and churn behavior. Quant Mark Econ 14, 325–351 (2016). https://doi.org/10.1007/s11129-016-9175-4
- Sales force
- Online transactions
- Revenue management
- Customer churn