New Customer Acquisition Strategy

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1 Page 1 New Customer Acquisition Strategy Based on Customer Profiling Segmentation and Scoring Model

2 Page 2 Introduction A customer profile is a snapshot of who your customers are, how to reach them, and why they buy from you. Deeper understandings of new and existing customers by collecting customer profile increases sales. This information is used to segment prospects more effectively, as well as send targeted communications more precisely to them. Accurate market segmentation is essential in successfully acquiring customers. Making the right strategic and operational decisions can result in higher lifetime customer value and increased customer satisfaction. Two main goals of market segmentation have remained constant over the years. At a strategic level, segmentation should help an organization rapidly evaluate new opportunities. At an operational level, segmentation efforts should yield information to help craft successful marketing offers to acquire prospects. The statistical scoring model generates a score value that indicates the probability of conversion based on segmented group characteristics. This provides a foundation for more advanced selection of potential customers. Statistical score value predict prospect behavior more accurately than judgmental score value, but require much more data, and staff allocation.

3 Page 3 Task To increase Sales Per Hour (SPH) by applying analytics. Challenge Customer Acquisition based on demographic and geographic characteristics, and time-periods optimization management for different segmentation approaches to define hot prospects Substantial difference in prospects behavior across various geographical parameters - time zone, province and postal code Identify hot acquisition targets by evaluating future potential of each segment Methodology I. Selecting the Data Source Data for modeling can be generated from number of sources. Those sources are two types: Internal and External. Internal data sources are those that are generated through activities undertaken by contact center solution providers like dialer transaction, agent performance etc. External data sources includes lead information, large prospect database etc. The data sets used to arrive at Analytical model for analysis are: i. Lead Information ii. Agent Background iii. Dialer Transaction iv. Job / Campaign Session i. Data Set: Lead Information Source: Customer Lead File Customer Lead File Lead ID List Code First Initials First Name Middle Name Last Name Phone Number Country State City Zip Code Time Zone Product Type Job ID Promotional Offer Discount Code Fields Description Lead Identifier Identification Number for each set of the Lead File Customer s First Initial (Mr., Mrs., etc.) First Name Middle Name Last Name Home Phone Number Name of Country Name of State or Province Name of City Zip Code World Time Zone Type of Service or Product Unique Job Identification Key Promotional Offer Codes if any Discounts Codes if any

4 Page 4 ii. Data Set: Agent Background Source: Agent Master Field Agent ID Agent Nme Gender Qualification Experience in Company Experience in Program Total Experience in the Industry Description Agent Identifier Name of the Agent Gender Last Completed Educational Qualification Work Experience in the Company in Months Work experience in the Program in Months Total Work Experience in the Industry in Months iii. Data Set: Dialer Transactions Source: Dialer* Field Lead ID Job ID Date Time Phone Number Agent ID Talk Time Update Time Disposition Code Call Type Flag Connect Description Lead Identifier Unique Job/Campaign Identifier Job/Campaign Name Date of Call Time of Call Customer s Phone Number Wait Time to Connect the Customer to the Agent Time Spent on Call Time Spent to Update Call Details Disposition Code Inbound / Outbound Call Was the Call Connected to the Agent or not * Dialer time zone to be indicated as well iv. Data Set: Job / Campaign Sessions Source: Dialer* Field Job ID Job Date Job Start Time Job End Time Agent ID Login Time Logout Time Idle Time OB Talk Time OB Update Time Description Unique Job / Campaign Identifier Job/Campaign Name Job Start Date Job / Campaign Start Time Job Campaign End Time Agent Identifier Agent Login Time Agent Logout Time Time Spent Idle Between Calls Talk Time on Outbound Call Update Time on Outbound Call

5 Page 5 I. Preparing the Data for Analysis Data preparation is the most important step in the analysis and model development process. In many cases it was necessary to combine data from several sources to create the final data set. Data cleansing is most important and time consuming process. This involves looking for and handling data errors, outliers and missing values. Data Errors and Outliers: An outlier is a single or low frequency occurrence of the value of a variable. Determining whether a value is an outlier or a data error is an art as well as a science. Having process knowledge of data is best strength. Missing Values: Missing values are present in almost every data set. The fact that a value is missing, however can be predictive. It is important to capture that information. II. Transforming the Variables Before making segmentation, there is a need to understand the data. For this purpose, variety of numerical summaries (including descriptive statistics such as averages, standard deviations, pivot tables and so forth) and study of distribution of the data is needed. Applying graphical and visualization tools to find new insights of different variables in the database and their importance for effective data analysis is useful. Sometimes, the format of a variable in the raw database is not sufficient for analysis. Hence, transform variables in accordance with the requirements of the algorithm chosen to build the model. III. Selecting the Contributing Variables In practice, all variables were not taken into consideration in the segmentation approach. One of the reasons being, the time it takes to build a model increases with the number of variables and another reason would be, blindly including extraneous columns which can lead to models with less rather than more predictive power. Some techniques are used to reduce the number of variables that eliminate marginal and unpredictive variables. IV. Segmentation Based on Contributing Variables Prospect segmentation is a process that divides prospects into smaller groups called segments. Segments are to be homogeneous within and desirably heterogeneous in between. In another words, prospects of the same segments possess the same or similar set of attributes. For example, forecasting on the basis of agent cluster rather than individual agent as a predictor variable. This yields more accurate results, which are parsimonious and are also easy to understand. For this purpose, we used different statistics and data-mining tools, such as, clustering (K-means, K- mediods, Kohonen feature maps) to represent a group of variables. V. Training and Testing Dataset Generation A subset or sample of the database needs to be selected to build models, which are capable of representing the whole population. Various techniques are available for generating the training and testing datasets, such as, stratified sampling, cluster sampling, sampling without replacement. Method selection depends on selection according to the nature of data. Generally, a stratified sampling technique is used to split into training and testing data set. VI. Scoring Model Building Model building is an iterative process. There is a need to explore alternative models to find the one that is most useful in solving the business problem. Searching for a good model may require us to go back and make some changes to the data or even modify the problem statement. We used logistic regression model. The logistic regression model computes the probability of the selected response as a function of the values of the predictor variables. VII. Scoring Model Testing We used different techniques to evaluate the model accuracy on test data set. One of the key measures is the Confusion Matrix. This shows the extent of type I, type II and overall errors. We consider a model is perfect if

6 Page 6 it shows more than 80% accuracy during testing. We also use different methods to test the model, such as, chisquare, p-value, odds ratio, and ROC curve. Results SAS Statistical Analysis Software was chosen as profiling, segmenting and scoring a prospect for this analysis. Predictive segmentation methods based on statistical analysis produced three optimal clusters on agent performance. These three different clusters classify agents as Very Good, Good and Average. Graphical view is depicted in Figure-1 People living within the same geographical boundaries exhibit similar buying patterns. Segmenting markets along geographical boundaries can lead to more specialized and focused marketing approaches. Geographical segmentation was done based on City and Zip Code etc.: o Four city based optimal clusters were performed to make the campaign strategy. Four groups characteristic is depicted in Figure-2 o Six zip code based optimal clusters were performed to make the campaign strategy. Six groups characteristic is depicted in Figure-3 Each segment s propensity to buy the services was used to evaluate the future potential of each segment. Past call transaction data was used to know the day of a week conversion trend and hour of a day conversion trend. This trend was utilized to call a prospect at proper time. A time zone mapping exercise was conducted to guide the dialing strategy. Graphical view is depicted in Figure-4. A standard definition of segments was developed which could be used for selecting potential prospects. Various groups could now more effectively collaborate around tactical dialing strategies. Scoring Model The scoring method was used to find out a score value of a prospect based on a collection of evidence as a whole while considering numerous dimensional groups. This provides a foundation for more advanced selection of potential prospects. Statistical score value predict prospect behavior more accurately than judgmental score value. Logistic Regression statistical model was used to score a prospect. A decile analysis was done to know the top scorer where conversion is high. It also helped to make an overall segment a

7 Page 7 prospect as hot, warm and cold. A graphical plot of decile chart is given below to visualize hot, warm and cold. Hot Prospect: Score Value ( and ) Warm Prospect: Score Value ( ) Cold Prospect: Score Value ( and ) allocation to convert a prospect to customer o Second stage allocates mixture proportion of very good, good and average agent group to hot, warm and cold prospect The product strategy group to define the product specifications and develope an Strategy Development Whom to call: Prospect selection strategy o Predict prospects score (based demographic variables, geographic variables) using scoring model. Find out which decile it belongs to and classify as hot, warm or cold prospect When to call: Dialing Strategy acquisition plan for a new product or service The campaign group to identify the best geographical area and partnership strategy The Analytics research team can drill down deeper into the segment for new insights about prospect attitudes towards support o Select weekday using Weekday time zone wise conversion trend table o Select hour of a day using Hourly time zone wise conversion trend table o Initial stage makes X number of dials to a particular prospect phone number to make maximum 3 connected dial o If prospect is not connected then make second stage Y dials after keeping an interval of time gap Who will handle the call: Agent allocation strategy o Initial stage selects Skill (Very good, good and Average) based agent W_CUSTACQ_1012