Why B2B Marketers should focus on Data Preparation

Why B2B Marketers should focus on Data Preparation
The backdrop of B2B marketing has now evolved & companies have started brainstorming about the swathe of their digital footprints, left on several platforms at web, which are utilized in creating hyper-targeted advertisements or campaigns to attract, engage & convert their potential buyers. Besides, tracing their digital footprints – to measure the endeavors of their marketing & quantify it, to relate to the business objectives (business KPIs) of organizations at large; the B2B marketers also track the vital data about their potential customers. The data related to demographics, technographics, firmographics, psychographic, “fit-data” as well as the past buying behaviors of the prospects & their research methodologies, are scrutinized to segment the prospects into several clusters, to hyper-target them. Artificial algorithms such as K-means clustering also help the marketers in hyper-targeting & converting their prospects through omnichannel marketing endeavors. Data preparation is the process of manipulating (or preprocessing or self-servicing) the raw, desperate & messy data in a form that can easily be analyzed for various business purposes (often in the form of structured data).  Data preparation happens to be the first crucial step in the projects where analyzing data is a sheer necessity & often includes several distinct tasks such as data ingestion, data fusion, data cleaning, data augmentation & data delivery. The several steps of data preparation may be sub-divided into the following categories:
  1. Systematic errors which include large volumes of data records, which often are from different sources
  2. Individual errors that affect a small number of data records which arise due to the errors in data entry
According to a report by SiriusDecisions: “The fundamental trouble with one-time data cleansing is that the day the project ends, the data is the cleanest it will be until the next round of contacts is added to the database”.  The absolute essentials to safeguard the digital footprints of a business include the following steps: 1. Data Security & Protection: Marketers need to make sure that their content is protected at all the times through granulated control systems 2. Brand Equity Management: The marketers need to ensure that the advertisements or the pieces of content that they create, is beneficial in establishing their brand equity 3. Futuristic Media Management Software: The media management software selected must be apt to support a wide array of files. Furthermore, the companies should have the right system & infrastructure in place to allow marketers to keep on expanding their collection of media data. What is Self-service data preparation?  Several companies provide software having visual interfaces to display the data & allow the users to directly explore, structure, clean, augment & update the data as per the business requirements. The users are provided with profiles & statistics about the content of the data. The data may also include semantic & machine learning algorithms that help the end-users in curating the data as per their business requirements. Tools such as IBM InfoSphere Advanced Data Preparation help the marketers in automating & operationalizing their data preparation. The efficiency rate associated with the use of the tool is also 10x times better as compared to the traditional tools. Conventional tools & technics such as scripting languages or ETL & Data Quality tools are not meant for business users. They require dexterous programming skills that most businesses often don’t have. Steps to Data Preparation According to a report by IBM, it has been estimated that data preparation accounts for about 80% of the total time of the data citizens. A data citizen is an employee who uses data from the digital footprints of prospects & from the first & third-party intent data, to make data-driven decisions to help the businesses accelerate on their core, bottom-line goals such as Conversion Rate Optimization (CRO) &/ sales conversions. The same report also states that automated data preparation will be utilized in 70% of the new data integration projects by the year 2020 The above insights imply that rather than spending the major chunk of their quality time in data mining (for preparing meaningful insights & predictive models to help business purposes), the marketers spend the majority of their quality time in decluttering, organizing & collecting data from omnichannel. The scenario looks concerning & essentially implies the need to modernize the approach of data preparation to hasten the process of data-driven decision making. Reducing the time to create insights from several data points will not only expedite the data-delivery process, but also will give the marketers more time for modeling precognitive marketing strategies, by tracing out the patterns generated by several first-party & third-party intent data (with the help of artificial intelligence (AI) & machine learning (ML) based automated & semi-automated platforms). Following are the steps to create a full-fledged framework for data preparation: 1. Data Exploration:  The data needs to be explored & the value of a particular data set needs to be determined. Data can be visualized with the help of interactive visuals such as column-based profiles, bar-graphs, histograms & line graphs. These data points can further be used for precise ML modeling & can be deployed to generate predictive models for precognitive marketing. 2. Changing of the Data Schema:  Data structuring is the process employed to change the form or schema of the businesses’ data & includes approaches such as splitting of the columns, satiating of hierarchies, inserting pivot tables for data classification & extraction of data into desired forms, along with the deletion of several unnecessary fields. Defining a proper structure to the data points assures that well-formatted tabular data is provided to the ML models. The structured data predictive transformation feature allows marketers to highlight segments of data with appropriate suggestions. As per the types of interactions that the marketers might have indulged with in the past, ML models help the marketers with most meaningful business insights. 3. Data Cleansing: A well-curated cleaning process is imperative to ML predictive modeling to achieve aptest & exact assumptions. During the cleaning process, the marketers need to identify the underlying issues with data quality such as – the missing or mismatched values. The mistakes need to be turfed-out of the data-sets by applying appropriate filters, making corrections &/ by deletion of erroneous values. 4. Data Enrichment: The ever-increasing digital footprint of prospects, as well as those from the B2B marketing endeavors, might be overwhelming for any system to process for meaningful insights unless they are standardized against some core data points. The process of standardization, coalescence & aggregation of data from omnichannel is also referred to as data enrichment. Enriching data assures that there is a synchronicity between several data points (from omnichannel marketing endeavors as well as from first & third-party intent data), which can be used to design, initiate as well as test-run several ML models, amalgamating data from multi-channel marketing.    Discrete data sets can be enriched by using lookups to the directories of data.  Union & intersection among several data points can also be easily established with the help of intelligent join & union interfaces that modern software possesses, to combine data points using the principles of ML. 5. Data Modeling: Several parallel modeling engines that are in-built with data preparation software can be used to build ML models by simultaneously connecting to several data points with a single click. After the cleansing of data, intuitive web-based interfaces allow automated ML models to generate predictive insights. Marketers can also write their predictive models which can then be evaluated by the ML-based modeling engines. 6. Data Validation: The ML-based modeling platforms are capable to search for the aptest permutations & combinations among the data points, through the coalescence of thousands of ML algorithms which process, transform, modify & standardize data to create the most insightful predictive models for precognitive marketing. The ML platforms are well-integrated with multiple data points, which expedite model evaluation & also provide a platform to compare several individual models. Split-testing several alternative models help the marketers figure out the data points which most pertinently resonate with their business objectives. 7. Data Adjustment for an automation modeling, which is both highly efficient & of a high-quality: Going by the common notion, it is anticipated that with automation & speed, the quality of data-driven insights is compromised upon. This may not be essentially true. Modern ML automated models also support manual tuning of data sets & allow data citizens to adjust the Ml algorithms for better-refined results in the due course of time, as & when required. Prediction targeting is something that needs constant up-gradation as the market trends keep on changing & so do the segments in which the data points are bifurcated. Therefore, split-test running new models continuously, in resonance with the specific data sets of high importance during a point in time, lies at the heart of effectively targeting the buyer personas using the precognitive marketing approaches. 8. Deployment of Predictive Models: The predictive models stand the test of time only when are beneficial in operationalizing the businesses. Several in-built ML-based predictive models can be deployed with a single click & can also be integrated with modern enterprise applications. This helps marketers to derive instant insights form businesses’ data without depending upon the scoring code & limitations of the underlying infrastructural constraints. The in-built ML models circulate application program interfaces (APIs) based on representation state transfer (REST).  RESTful web services allow the integration of enterprise applications, as they provide the architectural framework for designing APIs that, in turn, allow the end-users to interact with cloud-based services & break a transaction into several series of small modules – with each module capable of addressing a particular part of the holistic transaction module. Wrapping It Up Modern data preparation software need to comply with IT standards and at the same time also need to assure self-service assess of the predictive models. Stuart McDonald, Chief Marketing Officer (CMO) at Freshbooks, mentioned the power of data-driven business insights as follows:  “Tracking marketing is a cultural thing. Either tracking matters or it doesn’t. You’re in one camp or the other. Either you’re analytical and data-driven, or you go by what you think works. People who go by gut are wrong.” Transforming data points & having the liberty to modify & manipulate them as & when required is also imperative for the businesses to make effective data-driven decisions. The data from omnichannel marketing endeavors need to be incorporated with organization Business Intelligence (BI) & analytical efforts, for the most impactful predictive models to be established & tested in the due course. The conventional approaches of data modeling which comprise of SQL (Standardized Query language), Excel and coding can be intertwined with the modern data preparation strategies leveraging upon the multichannel marketing endeavors – to create the most relevant predictive models for driving the data-driven decision-making process by the B2B marketers. The modern BI tools also serve as excellent platforms for data visualization which further simplifies the underlying stories that the data-points narrate. Furthermore, the self-evolving ML algorithms help in scrutinizing the data quality & its relevance to the requirements of the modern businesses besides, helping in reducing the time for the preparation of dynamic data-points in real-time. We, at Valasys Media, utilize our wealth of knowledge, to help our B2B clients with hyper-targeted campaigns. Our lead generation, lead nurturing, account-based marketing, content syndication & list building services, all employ multichannel marketing to help our clients achieve their bottom-line goal of Conversion Rate Optimization (CRO). For further details about availing our services & how they are impactful in architecting a perennially healthy sales pipeline, aimed at the optimization of revenue for clients, feel free to get in touch with us.

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