diff --git a/lifetimes/utils.py b/lifetimes/utils.py
index 4e49fa73..91cc83ed 100644
--- a/lifetimes/utils.py
+++ b/lifetimes/utils.py
@@ -138,6 +138,9 @@ def _find_first_transactions(transactions, customer_id_col, datetime_col, moneta
     if observation_period_end is None:
         observation_period_end = transactions[datetime_col].max()
 
+    if isinstance(observation_period_end, pd.Period):
+        observation_period_end = observation_period_end.to_timestamp()
+
     select_columns = [customer_id_col, datetime_col]
 
     if monetary_value_col:
@@ -148,7 +151,7 @@ def _find_first_transactions(transactions, customer_id_col, datetime_col, moneta
     # make sure the date column uses datetime objects, and use Pandas' DateTimeIndex.to_period()
     # to convert the column to a PeriodIndex which is useful for time-wise grouping and truncating
     transactions[datetime_col] = pd.to_datetime(transactions[datetime_col], format=datetime_format)
-    transactions = transactions.set_index(datetime_col).to_period(freq)
+    transactions = transactions.set_index(datetime_col).to_period(freq).to_timestamp()
 
     transactions = transactions.loc[(transactions.index <= observation_period_end)].reset_index()
 
@@ -169,6 +172,9 @@ def _find_first_transactions(transactions, customer_id_col, datetime_col, moneta
     # mark the initial transactions as True
     period_transactions.loc[first_transactions, 'first'] = True
     select_columns.append('first')
+    # reset datetime_col to period
+    period_transactions[datetime_col] = pd.Index(period_transactions[datetime_col]).to_period(freq)
+
     return period_transactions[select_columns]
 
 
@@ -216,8 +222,9 @@ def summary_data_from_transaction_data(transactions, customer_id_col, datetime_c
 
     """
     if observation_period_end is None:
-        observation_period_end = transactions[datetime_col].max()
-    observation_period_end = pd.to_datetime(observation_period_end, format=datetime_format).to_period(freq)
+        observation_period_end = pd.to_datetime(transactions[datetime_col].max(), format=datetime_format).to_period(freq).to_timestamp()
+    else:
+        observation_period_end = pd.to_datetime(observation_period_end, format=datetime_format).to_period(freq).to_timestamp()
 
     # label all of the repeated transactions
     repeated_transactions = _find_first_transactions(
@@ -229,14 +236,17 @@ def summary_data_from_transaction_data(transactions, customer_id_col, datetime_c
         observation_period_end,
         freq
     )
+    # reset datetime_col to timestamp
+    repeated_transactions[datetime_col] = pd.Index(repeated_transactions[datetime_col]).to_timestamp()
+
     # count all orders by customer.
     customers = repeated_transactions.groupby(customer_id_col, sort=False)[datetime_col].agg(['min', 'max', 'count'])
 
     # subtract 1 from count, as we ignore their first order.
     customers['frequency'] = customers['count'] - 1
 
-    customers['T'] = (observation_period_end - customers['min']) / freq_multiplier
-    customers['recency'] = (customers['max'] - customers['min']) / freq_multiplier
+    customers['T'] = (observation_period_end - customers['min']) / np.timedelta64(1, freq) / freq_multiplier
+    customers['recency'] = (customers['max'] - customers['min']) / np.timedelta64(1, freq) / freq_multiplier
 
     summary_columns = ['frequency', 'recency', 'T']