顧客による誤用

返金したり、再販業者に依頼したり、トライアルを悪用されたりしないようにする方法をご紹介します。

Customer abuse is behavior that exploits your policies. These are the three common scenarios you might encounter:

Refund abuse
Resale abuse
Trial abuse

Use this guide to learn how to prevent, detect, and mitigate these types of abuse. Every business is different, you can adapt these suggestions to fit your needs.

Refund abuse

Refund abuse occurs when a customer takes advantage of your refund policy and causes disproportionate operational cost. For example, a customer might frequently return all purchased orders over a sustained period of time or request a refund for a shipment claiming they never received it despite having received their order.

注文を繰り返し返金した履歴のある顧客

返金が返金の多い顧客に集中しているかどうかを評価するには、注文金額を顧客が返金する頻度で割ります。返金する頻度を評価するには、次の Stripe Sigma クエリを使用します。

For example, if the output shows customers with 10 or more refunds per year account for most refunds but a small share of total orders, this possibly indicates abuse on a high level. However, we recommend you interpret these results in addition to your business context such as the customer lifetime value.

-- Order counts by customer refund frequency
WITH order_counts AS (
  SELECT 
    customer_id, -- This can be changed to another customer identifying attribute such as card, email, or shipping address
    COUNT(*) AS order_count,
    COUNT_IF(refunded) AS full_refund_count
  FROM charges
  WHERE captured
    AND created >= DATE_ADD('day', -365, CURRENT_DATE)
    AND dispute_id IS NULL -- Excludes disputes
  GROUP BY customer_id
),
order_count_frequency AS (
  SELECT 
    CASE 
      WHEN full_refund_count >= 10 THEN 10 
      WHEN customer_id IS NULL THEN 0 
      ELSE full_refund_count 
    END AS customer_refunded_x_or_more_orders,
    SUM(order_count) AS total_orders,
    SUM(full_refund_count) AS full_refund_orders
  FROM order_counts
  GROUP BY CASE 
    WHEN full_refund_count >= 10 THEN 10 
    WHEN customer_id IS NULL THEN 0 
    ELSE full_refund_count 
  END
),
order_count_frequency_aggregate_v1 AS (
  SELECT 
    *,
    SUM(total_orders) OVER (ORDER BY customer_refunded_x_or_more_orders DESC) AS total_orders_count,
    SUM(full_refund_orders) OVER (ORDER BY customer_refunded_x_or_more_orders DESC) AS total_full_refunds_count
  FROM order_count_frequency
  ORDER BY customer_refunded_x_or_more_orders
),
order_count_frequency_aggregate_v2 AS (
  SELECT 
    *,
    total_full_refunds_count / CAST(total_orders_count AS DOUBLE) AS refund_rate,
    total_full_refunds_count / CAST(SUM(full_refund_orders) OVER () AS DOUBLE) AS refunded_orders_recall,
    total_orders_count / CAST(SUM(total_orders) OVER () AS DOUBLE) AS total_orders_recall
  FROM order_count_frequency_aggregate_v1
)
SELECT 
  customer_refunded_x_or_more_orders,
  refund_rate, -- The rate of full refund orders divided by captured orders at each customer refund frequency and above
  refunded_orders_recall, -- Percentage of fully refunded orders out of all refunds 
  total_orders_recall, -- Percentage of total orders out of all total orders 
  total_orders_count, -- Number of all captured orders
  total_full_refunds_count -- Number of orders fully refunded
FROM order_count_frequency_aggregate_v2
ORDER BY customer_refunded_x_or_more_orders DESC

返金が頻繁に発生する顧客に対応するために、以下を行うことができます。

返金履歴とそのビジネスへの影響について、特定された顧客に通知する
今後の注文試行を制限する (一時的または永続的)
手数料を導入して繰り返しの返金を抑止

Resale abuse

Resale abuse is a customer who intends to resell the product to another person or business elsewhere such as a marketplace. For example, resellers might purchase limited-release goods in large quantities or create subscription accounts with discount codes in an effort to monetize from another buyer.

多くの場合、「パスワード共有」と呼ばれるのは、ログイン認証情報が共有され、他の人が 1 回の購入でメリットを得られる場合です。以下のようなアクティビティーを探します。

同時ログインセッション
地理的に分散した IP アドレスからの短時間のログイン

検出されると、メールや SMS のワンタイムパスコードなどの追加認証を実装して、手間を増やし、アカウント所有者を確認できます。

アカウント送金

リセラーは通常、手数料を支払って別の人にアカウントを送金します。以下のようなアクティビティーを探します。

管理していないマーケットプレイスで販売対象としてリストされているアカウント
同じカードまたはデバイス / ブラウザーのフィンガープリントを使用して作成されたアカウントが多く、該当する場合は同じプロモーションコードを使用することが多い
アカウント作成直後に別の地理的 IP アドレスからログインする
Screenshots shortly after account creation

ベロシティルールを作成して、潜在的なリセラーによって作成される可能性のある複数のアカウントをブロック、検出、または摩擦を作成します。Radar for Teamsを使用するルールの例を次に示します。

ルールの例	説明
`Review if :card_funding: = 'prepaid' and :total_customers_for_card_weekly: > 3 and not :is_off_session: and :charge_description: = “Individual Plan New Customer Discount"`	このルールは、顧客による直接アクションによってトリガーされ、直近 1 週間のカードごとの顧客の合計数が 3 を超え、特定の決済説明に一致するプリペイドカード決済をマークします。ビジネスの必要に応じて属性を変更できます。対応する属性の一覧をご覧ください。
`::customer_count_for_card_and_coupon_yearly:: > 3` の場合にブロックする	カードとクーポンの組み合わせごとに顧客数が 3 件を超える試行をブロックします。この属性はお客様が生成したものです。詳細については、メタデータ属性を参照してください。

Additionally, use Stripe Sigma to investigate suspected resold accounts and reconcile with your internal data to check for any policy abuse.

Trial abuse

Trial abuse occurs when customers cycle through free trials with no intent of converting. This can be expensive depending on your business due to the product usage cost. For example, these customers create multiple accounts to repeatedly gain access or use cards with no funds such that the payment fails at the end of the trial.

トライアル終了時の決済失敗

Trial abuse customers often sign up with prepaid or virtual debit cards that work for the start of the trial but fail at the end of the trial (commonly due to insufficient funds).

Stripe Radar offers a trial abuse control predicting the likelihood of payment failure at the end of a trial so you can block or pre-authorize signups, limit product access, or take other actions for high-risk trials. If interested, request to join the private preview to get access to these features.

メモ