Demand estimation for a new telecom service. Celebrating a decade of insight based consultancy in the MENA region

Transcription

1 Demand estimation for a new telecom service

2 Background & Need for research Client s objective introduce a Limited Mobility service within the telecom market: The service would be bundled along with a communication device similar to a mobile phone The device would be operational within a fixed radius from the base unit, thereby providing limited mobility to consumers. Research needed to address the following issues: Demand for such a Limited Mobility service Optimum combination of features for launch Cannibalization of current fixed line base

3 Not as simple as it sounds! Demand at various combinations of mobility, startup costs and call rates was needed. And there were more than 750 possible configurations of the service!!! Had to arrive at demand for all these from one study!!! Plus assess fixed line cannibalization for all these scenarios as well!!!

4 So how did we achieve the objective? We followed a modular approach: Show concept for LM. To familiarize respondents with the idea of Limited Mobility Conjoint Cannibalization assessment. Purchase intention for some of the Conjoint scenarios Predictive algorithm between conjoint utilities and purchase intent (Logistic Regression) Predict demand for LM for various offers.

5 First, we evaluated the attractiveness of the offer A concept was shown to familiarize respondents with the service idea and instrument Based on this data Utilities were generated for each attribute x level. Post this a Conjoint exercise was conducted: 5 attributes (Extent of mobility, Startup costs, Local/National/Mobile call rates) were shown to respondents, with 3-4 levels each. 16 product offers were generated and respondents were asked to rank these. E.g. Using the Utilities we were able to arrive at overall attractiveness (total utility) for any of the 750+ combinations of LM service. Extent of Mobility 2 km Start up cost 100 LE Minute rate to local 4 pt Minute rate to national 20 pt Mobile call rate 30 pt

6 Then we arrived at the demand Out of 16 offers, purchase intention was obtained for 8 randomly chosen ones. Logistic Regression was used to build a model between Total Utility of offer and Purchase Intention. Independent variables Dependent variable Mobility Startup cost Call rates PI Using likely purchase from this model and projecting to total household universe, we could predict how many consumers would buy LM - for any given combination of product features.

7 Over claim correction & Validation Purchase intention was measured using a point system. Point system used rather than a PI scale in order to: Get a wider spread of points And minimize tendency to choose most positive point on the scale. While using the prediction algorithm, the classification cut-off was set as high as 0.8 Only predicted values greater than 0.8 were classified as Purchase (1). Internal Validation - We looked at the LM combination that was closest to Mobile phones in terms of coverage, startup costs and call rates. The demand obtained for this combination was very similar to the mobile phone base in Egypt!!!

8 Lastly, we assessed the impact on fixed line A person could buy LM in 3 ways : Addition to fixed or mobile line. Replacement to fixed or mobile line. First line in case of non- of telecom. Our hypothesis - this choice would be influenced most by the coverage/mobility. Reason being that, mobility was the key differentiator between the three services (GSM, Fixed and LM) and hence the intrinsic value offered differs basis this parameter. Hence we captured the information whether people would buy this LM service as a replacement of current Fixed/Mobile line or as an additional line. This was combined with their current ship status to arrive at cannibalization estimates

9 Building the complete market picture Current status No line Only fixed line Only mobile line Both fixed & mobile line Will purchase LM or not obtained from modeling Yes No Yes No Yes No Yes No Replace or add Replace or add Replace or add Replace Add Replace Add Replace mobile Replace fixed Add LM Non LM LM + Fixed Fixed LM LM + Mobile Mobile LM + Fixed LM + Mobile LM + Mobile + Fixed Mobile +Fixed

10 A glimpse at the outputs Excel based simulator

11 In a nutshell Estimating demand as well as fixed line cannibalization for more than 750 possible combinations of the Limited Mobility service was our key challenge. Key innovation - combining Conjoint Analysis with Logistic Regression helped generate valid demand estimates for all these scenarios.