cohort_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/KelvinLam05/Cohort_Analysis/blob/main/cohort_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Goal of the project**"
      ],
      "metadata": {
        "id": "AlKsCuCudM0r"
      }
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      "cell_type": "markdown",
      "source": [
        "Customer cohort analysis examines differences between customers over time and is a powerful tool in guiding ecommerce acquisition strategy. Here's how it's done."
      ],
      "metadata": {
        "id": "-imMdsDmcv8o"
      }
    },
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      "source": [
        "**Data set information**"
      ],
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      "cell_type": "markdown",
      "source": [
        "Detailed data on sales of consumer goods obtained by ‘scanning’ the bar codes for individual products at electronic points of sale in a retail store. The data provide detailed information about quantities, characteristics and values of goods sold as well as their prices. The anonymized dataset includes 64.682 transactions of 5.242 SKU's sold to 22.625 customers during one year."
      ],
      "metadata": {
        "id": "3NSfcijdk5N6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the packages**"
      ],
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      }
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    {
      "cell_type": "code",
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      "source": [
        "# Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ecommercetools import utilities\n",
        "from ecommercetools import transactions\n",
        "from ecommercetools import customers"
      ],
      "metadata": {
        "id": "ACfOfAHjplZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the data**"
      ],
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        "outputId": "c8f8fade-66b9-42e2-fc50-387aff6c1fd5"
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      "source": [
        "# Load dataset\n",
        "filename = '/content/retail_scanner_data.csv'\n",
        "df = pd.read_csv(filename)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "   unnamed: 0        date  customer_id  ...    sku quantity sales_amount\n",
              "0           1  2016-02-01         2547  ...  0EM7L        1         3.13\n",
              "1           2  2016-02-01          822  ...  68BRQ        1         5.46\n",
              "2           3  2016-02-01         3686  ...  CZUZX        1         6.35\n",
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              "\n",
              "[5 rows x 8 columns]"
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      "cell_type": "markdown",
      "source": [
        "EcommerceTools requires that we standardise the column names in our transaction items dataframe."
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        "transaction_items = utilities.load_transaction_items(filename,\n",
        "                                                     date_column = 'date',\n",
        "                                                     order_id_column = 'transaction_id',\n",
        "                                                     customer_id_column = 'customer_id',\n",
        "                                                     sku_column = 'sku',\n",
        "                                                     quantity_column = 'quantity',\n",
        "                                                     unit_price_column = 'sales_amount')\n",
        "\n",
        "transaction_items.head()"
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      "execution_count": null,
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              "   unnamed: 0 order_date  customer_id  ...  quantity unit_price line_price\n",
              "0           1 2016-02-01         2547  ...         1       3.13       3.13\n",
              "1           2 2016-02-01          822  ...         1       5.46       5.46\n",
              "2           3 2016-02-01         3686  ...         1       6.35       6.35\n",
              "3           4 2016-02-01         3719  ...         1       5.59       5.59\n",
              "4           5 2016-02-01         9200  ...         1       6.88       6.88\n",
              "\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Create a customer cohort analysis dataset**"
      ],
      "metadata": {
        "id": "YUoiJzDAm_e5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The acquisition cohort is a label which identifies the period during which each customer was acquired. Depending on the size of your dataset and the frequency at which your customers order, you might want to use monthly, quarterly, or yearly cohorts. \n",
        "\n",
        "By default, this uses a monthly cohort, so a customer ordering on 2016-04-07 04:55:58 would be placed in a cohort called 2016-04. The new cohort labels get added to a DataFrame containing the unique customer_id, order_id, and order_date."
      ],
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        "cohorts_df = customers.get_cohorts(transaction_items, period = 'M')\n",
        "cohorts_df.head()"
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            "text/plain": [
              "   customer_id  order_id order_date acquisition_cohort order_cohort\n",
              "0         2547         1 2016-02-01            2016-01      2016-02\n",
              "1          822         2 2016-02-01            2016-01      2016-02\n",
              "2         3686         3 2016-02-01            2016-01      2016-02\n",
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              "4         9200         5 2016-02-01            2016-02      2016-02"
            ]
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    {
      "cell_type": "markdown",
      "source": [
        "**Create a customer \"retention\" dataset**"
      ],
      "metadata": {
        "id": "OQlXDemFoijC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we count how many of the customers acquired in a given cohort purchased again in subsequent periods. It’s common in cohort analysis to call this customer retention, but arguably it’s just a repurchase metric."
      ],
      "metadata": {
        "id": "WvtxfMkAoTQL"
      }
    },
    {
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      "source": [
        "retention_df = customers.get_retention(transaction_items)\n",
        "retention_df.head()"
      ],
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              "      <th>acquisition_cohort</th>\n",
              "      <th>order_cohort</th>\n",
              "      <th>customers</th>\n",
              "      <th>periods</th>\n",
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              "      <th>0</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "  acquisition_cohort order_cohort  customers  periods\n",
              "0            2016-01      2016-01       3817        0\n",
              "1            2016-01      2016-02       1104        1\n",
              "2            2016-01      2016-03        971        2\n",
              "3            2016-01      2016-04        899        3\n",
              "4            2016-01      2016-05        823        4"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a customer cohort analysis matrix**\n"
      ],
      "metadata": {
        "id": "pUF-RieXnIYw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will create a cohort matrix. This is a dataframe which shows each acquisition cohort on its own line and shows the number of customers who purchased in each subsequent period. If we set the percentage argument to True, the function will return the percentage of customers, instead of the raw numbers."
      ],
      "metadata": {
        "id": "Qw7gyRenCLJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cohort_matrix_df = customers.get_cohort_matrix(transaction_items, period = 'M', percentage = True)\n",
        "cohort_matrix_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Q5pbN7w4n0nS",
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      "execution_count": null,
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              "      <td>0.147248</td>\n",
              "      <td>0.154128</td>\n",
              "      <td>0.142202</td>\n",
              "      <td>0.149083</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5f7282a-6486-4451-b4fc-5d5f18a6bed5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5f7282a-6486-4451-b4fc-5d5f18a6bed5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5f7282a-6486-4451-b4fc-5d5f18a6bed5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "periods              0         1         2   ...        9         10        11\n",
              "acquisition_cohort                           ...                              \n",
              "2016-01             1.0  0.289232  0.254388  ...  0.166361  0.182604  0.187582\n",
              "2016-02             1.0  0.199348  0.186662  ...  0.167814  0.157666       NaN\n",
              "2016-03             1.0  0.216877  0.203131  ...  0.192822       NaN       NaN\n",
              "2016-04             1.0  0.190227  0.174084  ...       NaN       NaN       NaN\n",
              "2016-05             1.0  0.144495  0.144037  ...       NaN       NaN       NaN\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating cohort analysis heatmaps**"
      ],
      "metadata": {
        "id": "XPXJ7nITB1lk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we can create a heatmap to visualise our cohort. For each of the cohorts in our cohort matrix, this shows the percentage of the acquired customers who came back and purchased within a later period. "
      ],
      "metadata": {
        "id": "rKU7YgGpoW__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (18, 9))\n",
        "plt.title('Retention Rate per Cohorts')\n",
        "ax = sns.heatmap(cohort_matrix_df.iloc[:, 1:], annot = True, fmt = '.1%', linewidths = 1, linecolor = 'black')\n",
        "ax.tick_params(left = False, bottom = False); "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "jWgguVL0olKw",
        "outputId": "2019ee2a-af52-49a9-cff1-9f570f0879b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above retention table – Triangular chart, we can infer the following"
      ],
      "metadata": {
        "id": "YdlpAxc-Z138"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We see that the retention rates plunge after 1 month. Of the people that became our customer in January, only 28,9% makes another purchase the next month. \n",
        "\n",
        "* Customers acquired later on are churning faster. Of the people that became our customer in October, only 10.9% of the customers made a purchase 2 months after their initial purchase."
      ],
      "metadata": {
        "id": "ZdAU3rayPq6n"
      }
    }
  ]
}