Marketing Messages, Data Analysis & Data Mining

Understanding the Connection

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    Presentation Title

    Marketing Messages, Data Analysis & Data Mining

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    Overview

    An introduction to marketing strategies using data insights.

    Key Concepts

    Explores marketing messages, data analysis, and data mining.

    Business Application

    Focuses on how data shapes customer communication.

    Introduction

      Data-Driven Marketing

      Companies use data to inform their marketing messages.

      Insights from Data

      Data analysis and data mining provide valuable insights.

      Presentation Overview

      Explaining marketing messages, data analysis, and data mining.

      Connecting the Dots

      Understanding the interconnectedness of these concepts.

      Core Objectives

      Illustrate the significance of each concept for a marketing team.

      What Are Marketing Messages?

        Core Idea

        The main idea a brand wants to share with its customers.

        Brand Identity

        Includes what the brand stands for and what it offers.

        Customer Value

        Why the customer should care about the brand.

        Informative Messages

        Gives product information to the customer.

        Persuasive Messages

        Intended to make the customer want to buy.

        Marketing Message Types (Continued)

          Emotional Messages

          Connects with feelings and creates brand affinity.

          L'Oréal Example

          “Because You’re Worth It” – an emotional marketing message.

          Informative Example

          New Cleaning power - resolves most cleaning struggles

          Persuasive Example

          Sale! Get 30% off your first order

          Summary

          Diverse message types to cater to all consumer bases

          Why Are Marketing Messages Important?

            Grabbing Attention

            Clear messaging helps stand out in a noisy market.

            Audience Connection

            Makes people feel connected to the brand.

            Sales Increase

            A good message can influence buying decisions positively.

            Brand Image Building

            Strong messaging shapes customer perception of the company.

            Customer Relationships

            Clear messaging helps to build customer loyalty to the brand.

            What is Data Analysis?

              Definition

              Collecting, organizing, and studying data to get useful insights.

              Customer Preferences

              Helps companies understand what customers like.

              Product Performance

              Understanding which products are selling well.

              Sales Timing

              Identifying when people buy more.

              Tools Used

              Common tools include Excel, Tableau, and Google Data Studio.

              Data Analysis Example

                Sales Pattern

                Company observes most sales on weekends.

                Strategic Planning

                Company plans weekend discounts based on analysis.

                Data-Driven Decisions

                Using sales trends to optimize promotional activities.

                Revenue Growth

                Drive revenues by maximizing on peak sales times.

                Proactive approach

                Ensuring future campaigns are more effective.

                Why is Data Analysis Useful in Marketing?

                  Customer Behavior

                  Understand who buys what, when, and how.

                  Campaign Improvement

                  Testing which marketing messages work best.

                  Trend Identification

                  Plan future strategies based on identified trends.

                  Cost Savings

                  Avoid guesswork and make informed decisions.

                  Strategic Advantage

                  Helps companies in becoming more competitive.

                  What is Data Mining?

                    Definition

                    Searching huge amounts of data to find hidden patterns.

                    Advanced Analysis

                    More advanced than basic data analysis.

                    Predictive Use

                    Used for predicting customer behavior.

                    Customer Segmentation

                    Grouping similar customers together.

                    Sales Drop Analysis

                    Finding reasons for drops in sales.

                    Data Mining Example

                      Retail Insight

                      People who buy baby products often buy vitamins.

                      Cross-Promotion

                      Using this information for cross-promotional activities.

                      Increased revenue

                      Enhance sales through suggestive sales tactics.

                      Enhanced Customer Experience

                      Enhance customer experience by recommending needed items.

                      Improved Campaign Targeting

                      More effective marketing campaigns.

                      Data Analysis vs Data Mining

                        Feature

                        Goal

                        Data Analysis Goal

                        Understand current data

                        Data Mining Goal

                        Discover Hidden

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